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  • 101.
    Kennbäck, David
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk strålningsvetenskap.
    Practical implementation and exploration of dual energy computed tomography methods for Hounsfield units to stopping power ratio conversion2018Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    The purpose of this project was to explore the performance of methods for estimating stopping power ratio (SPR) from Hounsfield units (HU) using dual energy CT scans, rather than the standard single energy CT scans, with the aim of finding a method which could outperform the current single energy stoichiometric method. Such a method could reduce the margin currently added to the target volume during treatment which is defined as 3.5 % of the range to the target volume + 1 mm . Three such methods, by Taasti, Zhu, and, Lalonde and Bouchard, were chosen and implemented in MATLAB. A phantom containing 10 tissue-like inserts was scanned and used as a basis for the SPR estimation. To investigate the variation of the SPR from day-to-day the phantom was scanned once a day for 12 days. The resulting SPR of all methods, including the stoichiometric method, were compared with theoretical SPR values which were calculated using known elemental weight fractions of the inserts and mean excitation energies from the National Institute of Standards and Technology (NIST). It was found that the best performing method was the Taasti method which had, at best, an average percentage difference from the theoretical values of only 2.5 %. The Zhu method had, at best, 4.8 % and Lalonde-Bouchard 15.6% including bone tissue or 6.3 % excluding bone. The best average percentage difference of the stoichiometric method was 3.1 %.

    As the Taasti method was the best performing method and shows much promise, future work should focus on further improving its performance by testing more scanning protocols and kernels to find the ones yielding the best performance. This should then be supplemented with testing different pairs of energies for the dual energy scans. The fact that the Zhu and Lalonde-Bouchard method performed poorly could indicate problems with the implementation of those methods in this project. Investigating and solving those problems is also an important goal for future projects. Lastly the Lalonde-Bouchard method should be tested with more than two energy spectra.

  • 102. Khan, Mohammad A. U.
    et al.
    Niazi, M. Khalid Khan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Khan, M. Aurangzeb
    Ibrahim, M. Talal
    Endothelial Cell Image Enhancement using Non-subsampled Image Pyramid2007Inngår i: Information Technology Journal, ISSN 1812-5638, E-ISSN 1812-5646, Vol. 6, nr 7, s. 1057-1062Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A corneal endothelial cell image provides vast amount of information about a human eye. The cell density and cell shape parameters of a given endothelial cell image help opthamologists in making many vital clinical decisions. The acquired endothelial image is poor in contrast where cell boundaries are masked in the background. Previously, most of the work was based on morphological operations in spatial domain. However, if we think of cell structure as texture hidden in noisy background, we can get help from texture segmentation, a well studied area. In this work, we propose a non-subsampled Gaussian pyramid decomposition of lowpass region. At certain level of the pyramid, we start observing cleaner cell boundary structure which greatly facilitates its segmentation. Once segmented automatic cell counting can be used and simulation results have shown improvement in cell density count.

  • 103. Khonsari, R H
    et al.
    Friess, M
    Nysjö, Johan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Odri, G
    Malmberg, Filip
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Nyström, Ingela
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Messo, Elias
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Käkkirurgi.
    Hirsch, Jan M
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Käkkirurgi.
    Cabanis, E A M
    Kunzelmann, K H
    Salagnac, J M
    Corre, P
    Ohazama, A
    Sharpe, P T
    Charlier, P
    Olszewski, R
    Shape and volume of craniofacial cavities in intentional skull deformations2013Inngår i: American Journal of Physical Anthropology, ISSN 0002-9483, E-ISSN 1096-8644, Vol. 151, nr 1, s. 110-119Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Intentional cranial deformations (ICD) have been observed worldwide but are especially prevalent in preColombian cultures. The purpose of this study was to assess the consequences of ICD on three cranial cavities (intracranial cavity, orbits, and maxillary sinuses) and on cranial vault thickness, in order to screen for morphological changes due to the external constraints exerted by the deformation device. We acquired CT-scans for 39 deformed and 19 control skulls. We studied the thickness of the skull vault using qualitative and quantitative methods. We computed the volumes of the orbits, of the maxillary sinuses, and of the intracranial cavity using haptic-aided semi-automatic segmentation. We finally defined 3D distances and angles within orbits and maxillary sinuses based on 27 anatomical landmarks and measured these features on the 58 skulls. Our results show specific bone thickness patterns in some types of ICD, with localized thinning in regions subjected to increased pressure and thickening in other regions. Our findings confirm that volumes of the cranial cavities are not affected by ICDs but that the shapes of the orbits and of the maxillary sinuses are modified in circumferential deformations. We conclude that ICDs can modify the shape of the cranial cavities and the thickness of their walls but conserve their volumes. These results provide new insights into the morphological effects associated with ICDs and call for similar investigations in subjects with deformational plagiocephalies and craniosynostoses.

  • 104.
    Khonsari, Roman H.
    et al.
    Assistance Publique – Hôpitaux de Paris, Hôpital Necker Enfants-Malades, Service de Chirurgie Maxillo-faciale et Plastique, Université Paris-Descartes, Paris, France.
    Way, Benjamin
    The Craniofacial Unit, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom.
    Nysjö, Johan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion.
    Odri, Guillaume A.
    Assistance Publique – Hôpitaux de Paris, Hôpital Lariboisière, Service de Chirurgie Orthopédique, Université Paris-Diderot, Paris, France.
    Olszewski, Raphaël
    Department of Oral and Maxillofacial Surgery, Saint-Luc University Hospital, Catholic University of Leuven, Brussels, Belgium.
    Evans, Robert D.
    The Craniofacial Unit, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom.
    Dunaway, David J.
    The Craniofacial Unit, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom.
    Nyström, Ingela
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion.
    Britto, Jonathan A.
    The Craniofacial Unit, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom.
    Fronto-facial advancement and bipartition in Crouzon-Pfeiffer and Apert syndromes: Impact of fronto-facial surgery upon orbital and airway parameters in FGFR2 syndromes2016Inngår i: Journal of Cranio-Maxillofacial Surgery, ISSN 1010-5182, E-ISSN 1878-4119, Vol. 44, nr 10, s. 1567-1575Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A major concern in FGFR2 craniofaciosynostosis is oculo-orbital disproportion, such that orbital malformation provides poor accommodation and support for the orbital contents and peri-orbita, leading to insufficient eyelid closure, corneal exposure and eventually to functional visual impairment. Fronto-facial monobloc osteotomy followed by distraction osteogenesis aims to correct midfacial growth deficiencies in Crouzon–Pfeiffer syndrome patients. Fronto-facial bipartition osteotomy followed by distraction is a procedure of choice in Apert syndrome patients. These procedures modify the shape and volume of the orbit and tend to correct oculo-orbital disproportion. Little is known about the detailed 3D shape of the orbital phenotype in CPS and AS, and about how this is modified by fronto-facial surgery.

    Twenty-eight patients with CMS, 13 patients with AS and 40 control patients were included. CT scans were performed before and after fronto-facial surgery. Late post-operative scans were available for the Crouzon–Pfeiffer syndrome group. Orbital morphology was investigated using conventional three-dimensional cephalometry and shape analysis after mesh-based segmentation of the orbital contents.

    We characterized the 3D morphology of CPS and AS orbits and showed how orbital shape is modified by surgery. We showed that monobloc-distraction in CPS and bipartition-distraction in AS specifically address the morphological characteristics of the two syndromes.

  • 105.
    Khonsari, Roman
    et al.
    CHU Pitié-Salpêtrière, Paris, France.
    Nysjö, Johan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion.
    Way, Benjamin
    Great Ormond Street Hospital, London, United-Kingdom.
    Karunakaran, Tharsika
    Great Ormond Street Hospital, London, United-Kingdom.
    Nyström, Ingela
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion.
    Odri, Guillaume
    University of Nantes, Nantes, France.
    Dunaway, David
    Great Ormond Street Hospital, London, United-Kingdom.
    Evans, R.
    Great Ormond Street Hospital, London, United-Kingdom.
    Olszewski, Raphael
    University of Leuven, Brussels, Belgium.
    Britto, Jonathan
    Great Ormond Street Hospital, London, United-Kingdom.
    Orbital morphology in Crouzon-Pfeiffer and Apert syndromes before and after surgical correction:study by 3D cephalometry, semi-automatic segmentation and 3D shape comparison2014Inngår i: Proc. Computer Assisted Radiology and Surgery (CARS). Fukuoka, Japan, June 25-28, Springer, 2014, s. 192-193Konferansepaper (Fagfellevurdert)
  • 106. Kim, Tae-Yun
    et al.
    Cho, Nam-Hoon
    Jeong, Goo-Bo
    Bengtsson, Ewert
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Choi, Heung-Kook
    3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading2014Inngår i: Computational & Mathematical Methods in Medicine, ISSN 1748-670X, E-ISSN 1748-6718, Vol. 2014, s. 536217:1-12Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system.

  • 107.
    Klemm, Anna H.
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Thomae, Andreas W.
    Wachal, Katarina
    Dietzel, Steffen
    Tracking Microscope Performance: A Workflow to Compare Point Spread Function Evaluations Over Time2019Inngår i: Microscopy and Microanalysis, ISSN 1431-9276, E-ISSN 1435-8115, Vol. 25, nr 3, s. 699-704Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Routine system checks are essential for supervising the performance of an advanced light microscope. Recording and evaluating the point spread function (PSF) of a given system provides information about the resolution and imaging. We compared the performance of fluorescent and gold beads for PSF recordings. We then combined the open-source evaluation software PSFj with a newly developed KNIME pipeline named PSFtracker to create a standardized workflow to track a system's performance over several measurements and thus over long time periods. PSFtracker produces example images of recorded PSFs, plots full-width-half-maximum (FWHM) measurements over time and creates an html file which embeds the images and plots, together with a table of results. Changes of the PSF over time are thus easily spotted, either in FWHM plots or in the time series of bead images which allows recognition of aberrations in the shape of the PSF. The html file, viewed in a local browser or uploaded on the web, therefore provides intuitive visualization of the state of the PSF over time. In addition, uploading of the html file on the web allows other microscopists to compare such data with their own.

  • 108.
    Koos, Björn
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylära verktyg. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Kamali-Moghaddam, Masood
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylära verktyg. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    David, Leonor
    Sobrinho-Simoes, Manuel
    Dimberg, Anna
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Vaskulärbiologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Nilsson, Mats
    Wählby, Carolina
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Söderberg, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylära verktyg. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Next-Generation Pathology: Surveillance of Tumor Microecology2015Inngår i: Journal of Molecular Biology, ISSN 0022-2836, E-ISSN 1089-8638, Vol. 427, nr 11, s. 2013-2022Artikkel, forskningsoversikt (Fagfellevurdert)
    Abstract [en]

    A tumor is a heterogeneous population of cells that provides an environment in which every cell resides in a microenvironmental niche. Microscopic evaluation of tissue sections, based on histology and immunohistochemistry, has been a cornerstone in pathology for decades. However, the dawn of novel technologies to investigate genetic aberrations is currently adopted in routine molecular pathology. We herein describe our view on how recent developments in molecular technologies, focusing on proximity ligation assay and padlock probes, can be applied to merge the two branches of pathology, allowing molecular profiling under histologic observation. We also discuss how the use of image analysis will be pivotal to obtain information at a cellular level and to interpret holistic images of tissue sections. By understanding the cellular communications in the microecology of tumors, we will be at a better position to predict disease progression and response to therapy.

  • 109.
    Koriakina, Nadezhda
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sladoje, Natasa
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Bengtsson, Ewert
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.
    Ramqvist, Eva Darai
    Pathology and Cytology, Karolinska Institute, Stockholm, Sweden.
    Hirsch, Jan M.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Käkkirurgi.
    Runow Stark, Christina
    Public Dental Service, Södersjukhuset, Stockholm, Sweden.
    Lindblad, Joakim
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion.
    Visualization of convolutional neural network class activations in automated oral cancer detection for interpretation of malignancy associated changes2019Inngår i: 3rd NEUBIAS Conference, Luxembourg, 2-8 February 2019, 2019, , s. 1Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    Introduction: Cancer of the oral cavity is one of the most common malignancies in the world. The incidence of oral cavity and oropharyngeal cancer is increasing among young people. It is noteworthy that the oral cavity can be relatively easily accessed for routine screening tests that could potentially decrease the incidence of oral cancer. Automated deep learning computer aided methods show promising ability for detection of subtle precancerous changes at a very early stage, also when visual examination is less effective. Although the biological nature of these malignancy associated changes is not fully understood, the consistency of morphology and textural changes within a cell dataset could shed light on the premalignant state. In this study, we are aiming to increase understanding of this phenomenon by exploring and visualizing what parts of cell images are considered as most important when trained deep convolutional neural networks (DCNNs) are used to differentiate cytological images into normal and abnormal classes.

    Materials and methods: Cell samples are collected with a brush at areas of interest in the oral cavity and stained according to standard PAP procedures. Digital images from the slides are acquired with a 0.32 micron pixel size in greyscale format (570 nm bandpass filter). Cell nuclei are manually selected in the images and a small region is cropped around each nucleus resulting in images of 80x80 pixels. Medical knowledge is not used for choosing the cells but they are just randomly selected from the glass; for the learning process we are only providing ground truth on the patient level and not on the cell level. Overall, 10274 images of cell nuclei and the surrounding region are used to train state-of-the-art DCNNs to distinguish between cells from healthy persons and persons with precancerous lesions. Data augmentation through 90 degrees rotations and mirroring is applied to the datasets. Different approaches for class activation mapping and related methods are utilized to determine what image regions and feature maps are responsible for the relevant class differentiation.

    Results and Discussion:The best performing of the observed deep learning architectures reaches a per cell classification accuracy surpassing 80% on the observed material. Visualizing the class activation maps confirms our expectation that the network is able to learn to focus on specific relevant parts of the sample regions. We compare and evaluate our findings related to detected discriminative regions with the subjective judgements of a trained cytotechnologist. We believe that this effort on improving understanding of decision criteria used by machine and human leads to increased understanding of malignancy associated changes and also improves robustness and reliability of the automated malignancy detection procedure.

  • 110.
    Kullberg, Joel
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för onkologi, radiologi och klinisk immunologi, Enheten för radiologi.
    Ahlström, Håkan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för onkologi, radiologi och klinisk immunologi, Enheten för radiologi.
    Johansson, Lars
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för onkologi, radiologi och klinisk immunologi, Enheten för radiologi.
    Frimmel, Hans
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för onkologi, radiologi och klinisk immunologi, Enheten för radiologi. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för teknisk databehandling. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Automated and reproducible segmentation of visceral and subcutaneous adipose tissue from abdominal MRI2007Inngår i: International Journal of Obesity, ISSN 0307-0565, E-ISSN 1476-5497, Vol. 31, s. 1806-1817Artikkel i tidsskrift (Fagfellevurdert)
  • 111.
    Kullberg, Joel
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för onkologi, radiologi och klinisk immunologi, Enheten för radiologi.
    Angelhed, Jan-Erik
    Lönn, Lars
    Brandberg, John
    Ahlström, Håkan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för onkologi, radiologi och klinisk immunologi, Enheten för radiologi.
    Frimmel, Hans
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för onkologi, radiologi och klinisk immunologi, Enheten för radiologi. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för teknisk databehandling. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Johansson, Lars
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för onkologi, radiologi och klinisk immunologi, Enheten för radiologi.
    Whole-body T1 mapping improves the definition of adipose tissue: Consequences for automated image analysis2006Inngår i: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 24, s. 394-401Artikkel i tidsskrift (Fagfellevurdert)
  • 112.
    Kullberg, Joel
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för onkologi, radiologi och klinisk immunologi, Enheten för radiologi.
    Brandberg, John
    Angelhed, Jan-Erik
    Frimmel, Hans
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för onkologi, radiologi och klinisk immunologi, Enheten för radiologi. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för teknisk databehandling. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datoriserad bildanalys.
    Bergelin, Eva
    Strid, Lena
    Ahlström, Håkan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för onkologi, radiologi och klinisk immunologi, Enheten för radiologi.
    Johansson, Lars
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för onkologi, radiologi och klinisk immunologi, Enheten för radiologi.
    Lönn, Lars
    Whole-body adipose tissue analysis: Comparison of MRI, CT and dual energy X-ray absorptiometry2009Inngår i: British Journal of Radiology, ISSN 0007-1285, E-ISSN 1748-880X, Vol. 82, s. 123-130Artikkel i tidsskrift (Fagfellevurdert)
  • 113. Kumar, Abhishek
    et al.
    Agarwala, Sunita
    Dhara, Ashis Kumar
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Mukhopadhyay, Sudipta
    Nandi, Debashis
    Garg, Mandeep
    Khandelwal, Niranjan
    Kalra, Naveen
    Localization of lung fields in HRCT images using a deep convolution neural network2018Inngår i: Medical Imaging 2018: Computer-Aided Diagnosis, Bellingham, WA, 2018, s. 1057535:1-8, artikkel-id 1057535Konferansepaper (Fagfellevurdert)
  • 114.
    Kylberg, Gustaf
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Automatic Virus Identification using TEM: Image Segmentation and Texture Analysis2014Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Viruses and their morphology have been detected and studied with electron microscopy (EM) since the end of the 1930s. The technique has been vital for the discovery of new viruses and in establishing the virus taxonomy. Today, electron microscopy is an important technique in clinical diagnostics. It both serves as a routine diagnostic technique as well as an essential tool for detecting infectious agents in new and unusual disease outbreaks.

    The technique does not depend on virus specific targets and can therefore detect any virus present in the sample. New or reemerging viruses can be detected in EM images while being unrecognizable by molecular methods.

    One problem with diagnostic EM is its high dependency on experts performing the analysis. Another problematic circumstance is that the EM facilities capable of handling the most dangerous pathogens are few, and decreasing in number.

    This thesis addresses these shortcomings with diagnostic EM by proposing image analysis methods mimicking the actions of an expert operating the microscope. The methods cover strategies for automatic image acquisition, segmentation of possible virus particles, as well as methods for extracting characteristic properties from the particles enabling virus identification.

    One discriminative property of viruses is their surface morphology or texture in the EM images. Describing texture in digital images is an important part of this thesis. Viruses show up in an arbitrary orientation in the TEM images, making rotation invariant texture description important. Rotation invariance and noise robustness are evaluated for several texture descriptors in the thesis. Three new texture datasets are introduced to facilitate these evaluations. Invariant features and generalization performance in texture recognition are also addressed in a more general context.

    The work presented in this thesis has been part of the project Panvirshield, aiming for an automatic diagnostic system for viral pathogens using EM. The work is also part of the miniTEM project where a new desktop low-voltage electron microscope is developed with the aspiration to become an easy to use system reaching high levels of automation for clinical tissue sections, viruses and other nano-sized particles.

    Delarbeid
    1. Evaluation of noise robustness for local binary pattern descriptors in texture classification
    Åpne denne publikasjonen i ny fane eller vindu >>Evaluation of noise robustness for local binary pattern descriptors in texture classification
    2013 (engelsk)Inngår i: EURASIP Journal on Image and Video Processing, ISSN 1687-5176, E-ISSN 1687-5281, nr 17Artikkel i tidsskrift (Fagfellevurdert) Published
    Abstract [en]

    Local binary pattern (LBP) operators have become commonly used texture descriptors in recent years. Several new LBP-based descriptors have been proposed, of which some aim at improving robustness to noise. To do this, the thresholding and encoding schemes used in the descriptors are modified. In this article, the robustness to noise for the eight following LBP-based descriptors are evaluated; improved LBP, median binary patterns (MBP), local ternary patterns (LTP), improved LTP (ILTP), local quinary patterns, robust LBP, and fuzzy LBP (FLBP). To put their performance into perspective they are compared to three well-known reference descriptors; the classic LBP, Gabor filter banks (GF), and standard descriptors derived from gray-level co-occurrence matrices. In addition, a roughly five times faster implementation of the FLBP descriptor is presented, and a new descriptor which we call shift LBP is introduced as an even faster approximation to the FLBP. The texture descriptors are compared and evaluated on six texture datasets; Brodatz, KTH-TIPS2b, Kylberg, Mondial Marmi, UIUC, and a Virus texture dataset. After optimizing all parameters for each dataset the descriptors are evaluated under increasing levels of additive Gaussian white noise. The discriminating power of the texture descriptors is assessed using tenfolded cross-validation of a nearest neighbor classifier. The results show that several of the descriptors perform well at low levels of noise while they all suffer, to different degrees, from higher levels of introduced noise. In our tests, ILTP and FLBP show an overall good performance on several datasets. The GF are often very noise robust compared to the LBP-family under moderate to high levels of noise but not necessarily the best descriptor under low levels of added noise. In our tests, MBP is neither a good texture descriptor nor stable to noise.

    sted, utgiver, år, opplag, sider
    Springer, 2013
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-203664 (URN)10.1186/1687-5281-2013-17 (DOI)000321866700001 ()
    Tilgjengelig fra: 2013-07-17 Laget: 2013-07-17 Sist oppdatert: 2018-01-11bibliografisk kontrollert
    2. Regional Zernike Moments for Texture Recognition
    Åpne denne publikasjonen i ny fane eller vindu >>Regional Zernike Moments for Texture Recognition
    2012 (engelsk)Inngår i: Proceedings of the 21st International Conference on Pattern Recognition (ICPR), 2012, s. 1635-1638Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

     Zernike moments are commonly used in pattern recognition but are not suited for texture analysis. In this paper we introduce regional Zernike moments (RZM) where we combine the Zernike moments for the pixels in a region to create a measure suitable for texture analysis. We compare our proposed measures to texture measures based on Gabor filters, Haralick co-occurrence matrices and local binary patterns on two different texture image sets, and show that they are noise insensitive and very well suited for texture recognition.

    Emneord
    Statistical, Syntactic and Structural Pattern Recognition, Segmentation, Color and Texture, Classification and Clustering
    HSV kategori
    Forskningsprogram
    Datoriserad bildanalys; Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-188373 (URN)
    Konferanse
    ICPR 2012
    Tilgjengelig fra: 2012-12-17 Laget: 2012-12-17 Sist oppdatert: 2018-01-11
    3. Comparing Rotation Invariance and Interpolation Methods in Texture Recognition Based on Local Binary Pattern Features
    Åpne denne publikasjonen i ny fane eller vindu >>Comparing Rotation Invariance and Interpolation Methods in Texture Recognition Based on Local Binary Pattern Features
    (engelsk)Artikkel i tidsskrift (Fagfellevurdert) Submitted
    HSV kategori
    Forskningsprogram
    Datoriserad bildanalys; Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-217327 (URN)
    Tilgjengelig fra: 2014-02-02 Laget: 2014-02-02 Sist oppdatert: 2014-04-29
    4. Exploring Filter Banks Based on Orthogonal Moments for Texture Recognition
    Åpne denne publikasjonen i ny fane eller vindu >>Exploring Filter Banks Based on Orthogonal Moments for Texture Recognition
    (engelsk)Manuskript (preprint) (Annet vitenskapelig)
    HSV kategori
    Forskningsprogram
    Datoriserad bildanalys; Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-188371 (URN)
    Tilgjengelig fra: 2014-02-02 Laget: 2012-12-17 Sist oppdatert: 2014-04-29
    5. A Note on: Invariant Features, Overfitting and Generalization Performance in Texture Recognition
    Åpne denne publikasjonen i ny fane eller vindu >>A Note on: Invariant Features, Overfitting and Generalization Performance in Texture Recognition
    (engelsk)Manuskript (preprint) (Annet vitenskapelig)
    HSV kategori
    Forskningsprogram
    Datoriserad bildanalys; Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-217326 (URN)
    Tilgjengelig fra: 2014-02-02 Laget: 2014-02-02 Sist oppdatert: 2014-04-29
    6. Kylberg Texture Dataset v. 1.0
    Åpne denne publikasjonen i ny fane eller vindu >>Kylberg Texture Dataset v. 1.0
    2011 (engelsk)Rapport (Annet vitenskapelig)
    sted, utgiver, år, opplag, sider
    Uppsala: Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University, 2011. s. 4
    Serie
    External report (Blue series) ; 35
    Emneord
    texture dataset
    HSV kategori
    Forskningsprogram
    Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-163125 (URN)
    Tilgjengelig fra: 2011-12-08 Laget: 2011-12-08 Sist oppdatert: 2018-01-12bibliografisk kontrollert
    7. Towards Automated TEM for Virus Diagnostics: Segmentation of Grid Squares and Detection of Regions of Interest
    Åpne denne publikasjonen i ny fane eller vindu >>Towards Automated TEM for Virus Diagnostics: Segmentation of Grid Squares and Detection of Regions of Interest
    2009 (engelsk)Inngår i: Proceedings of the 16th Scandinavian Conference on Image Analysis (SCIA), Berlin: Springer-Verlag , 2009, s. 169-178Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    When searching for viruses in an electron microscope thesample grid constitutes an enormous search area. Here, we present methodsfor automating the image acquisition process for an automatic virusdiagnostic application. The methods constitute a multi resolution approachwhere we first identify the grid squares and rate individual gridsquares based on content in a grid overview image and then detect regionsof interest in higher resolution images of good grid squares. Our methodsare designed to mimic the actions of a virus TEM expert manually navigatingthe microscope and they are also compared to the expert’s performance.Integrating the proposed methods with the microscope wouldreduce the search area by more than 99.99% and it would also removethe need for an expert to perform the virus search by the microscope.

    sted, utgiver, år, opplag, sider
    Berlin: Springer-Verlag, 2009
    Serie
    Lecture Notes in Computer Science, ISSN 0302-9743 ; 5575
    Emneord
    TEM, virus diagnostics, automatic image acquisition
    HSV kategori
    Forskningsprogram
    Datoriserad bildanalys
    Identifikatorer
    urn:nbn:se:uu:diva-108568 (URN)10.1007/978-3-642-02230-2_18 (DOI)978-3-642-02229-6 (ISBN)
    Tilgjengelig fra: 2009-09-22 Laget: 2009-09-22 Sist oppdatert: 2018-01-13
    8. Segmentation of virus particle candidates in transmission electron microscopy images
    Åpne denne publikasjonen i ny fane eller vindu >>Segmentation of virus particle candidates in transmission electron microscopy images
    Vise andre…
    2012 (engelsk)Inngår i: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 245, nr 2, s. 140-147Artikkel i tidsskrift (Fagfellevurdert) Published
    Abstract [en]

    In this paper, we present an automatic segmentation method that detects virus particles of various shapes in transmission electron microscopy images. The method is based on a statistical analysis of local neighbourhoods of all the pixels in the image followed by an object width discrimination and finally, for elongated objects, a border refinement step. It requires only one input parameter, the approximate width of the virus particles searched for. The proposed method is evaluated on a large number of viruses. It successfully segments viruses regardless of shape, from polyhedral to highly pleomorphic.

    sted, utgiver, år, opplag, sider
    Blackwell Publishing, 2012
    Emneord
    radial density profile, transmission electron microscopy, virus detection, virus segmentation
    HSV kategori
    Forskningsprogram
    Datoriserad bildanalys; Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-163761 (URN)10.1111/j.1365-2818.2011.03556.x (DOI)000298987100004 ()
    Tilgjengelig fra: 2011-10-04 Laget: 2011-12-14 Sist oppdatert: 2017-12-08bibliografisk kontrollert
    9. Virus texture analysis using local binary patterns and radial density profiles
    Åpne denne publikasjonen i ny fane eller vindu >>Virus texture analysis using local binary patterns and radial density profiles
    2011 (engelsk)Inngår i: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications / [ed] San Martin, César; Kim, Sang-Woon, Springer Berlin/Heidelberg, 2011, s. 573-580Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    We investigate the discriminant power of two local and two global texture measures on virus images. The viruses are imaged using negative stain transmission electron microscopy. Local binary patterns and a multi scale extension are compared to radial density profiles in the spatial domain and in the Fourier domain. To assess the discriminant potential of the texture measures a Random Forest classifier is used. Our analysis shows that the multi scale extension performs better than the standard local binary patterns and that radial density profiles in comparison is a rather poor virus texture discriminating measure. Furthermore, we show that the multi scale extension and the profiles in Fourier domain are both good texture measures and that they complement each other well, that is, they seem to detect different texture properties. Combining the two, hence, improves the discrimination between virus textures.

    sted, utgiver, år, opplag, sider
    Springer Berlin/Heidelberg, 2011
    Serie
    Lecture Notes in Computer Science, ISSN 0302-9743 ; 7042
    Emneord
    virus morphology, texture analysis, local binary patterns, radial density profiles
    HSV kategori
    Forskningsprogram
    Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-163119 (URN)10.1007/978-3-642-25085-9_68 (DOI)978-3-642-25084-2 (ISBN)
    Konferanse
    16th Iberoamerican Congress on Pattern Recognition
    Tilgjengelig fra: 2011-12-08 Laget: 2011-12-08 Sist oppdatert: 2018-01-12bibliografisk kontrollert
    10. Virus recognition based on local texture
    Åpne denne publikasjonen i ny fane eller vindu >>Virus recognition based on local texture
    2014 (engelsk)Inngår i: Proceedings 22nd International Conference on Pattern Recognition (ICPR), 2014, 2014, s. 3227-3232Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    To detect and identify viruses in electron microscopy images is crucial in certain clinical emergency situations. It is currently a highly manual task, requiring an expert sittingat the microscope to perform the analysis visually. Here wefocus on and investigate one aspect towards automating the virusdiagnostic task, namely recognizing the virus type based on theirtexture once possible virus objects have been segmented. Weshow that by using only local texture descriptors we achievea classification rate of almost 89% on texture patches from 15different virus types and a debris (false object) class. We compareand combine 5 different types of local texture descriptors andshow that by combining the different types a lower classificationerror is achieved. We use a Random Forest Classifier and comparetwo approaches for feature selection.

    Serie
    International Conference on Pattern Recognition, ISSN 1051-4651
    HSV kategori
    Forskningsprogram
    Datoriserad bildanalys; Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-216290 (URN)10.1109/ICPR.2014.556 (DOI)000359818003060 ()978-1-4799-5208-3 (ISBN)
    Konferanse
    IEEE 22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden
    Tilgjengelig fra: 2014-02-02 Laget: 2014-01-20 Sist oppdatert: 2018-01-11bibliografisk kontrollert
  • 115.
    Kylberg, Gustaf
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    A Note on: Invariant Features, Overfitting and Generalization Performance in Texture RecognitionManuskript (preprint) (Annet vitenskapelig)
  • 116.
    Kylberg, Gustaf
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Comparing Rotation Invariance and Interpolation Methods in Texture Recognition Based on Local Binary Pattern FeaturesArtikkel i tidsskrift (Fagfellevurdert)
  • 117.
    Kylberg, Gustaf
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Evaluation of noise robustness for local binary pattern descriptors in texture classification2013Inngår i: EURASIP Journal on Image and Video Processing, ISSN 1687-5176, E-ISSN 1687-5281, nr 17Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Local binary pattern (LBP) operators have become commonly used texture descriptors in recent years. Several new LBP-based descriptors have been proposed, of which some aim at improving robustness to noise. To do this, the thresholding and encoding schemes used in the descriptors are modified. In this article, the robustness to noise for the eight following LBP-based descriptors are evaluated; improved LBP, median binary patterns (MBP), local ternary patterns (LTP), improved LTP (ILTP), local quinary patterns, robust LBP, and fuzzy LBP (FLBP). To put their performance into perspective they are compared to three well-known reference descriptors; the classic LBP, Gabor filter banks (GF), and standard descriptors derived from gray-level co-occurrence matrices. In addition, a roughly five times faster implementation of the FLBP descriptor is presented, and a new descriptor which we call shift LBP is introduced as an even faster approximation to the FLBP. The texture descriptors are compared and evaluated on six texture datasets; Brodatz, KTH-TIPS2b, Kylberg, Mondial Marmi, UIUC, and a Virus texture dataset. After optimizing all parameters for each dataset the descriptors are evaluated under increasing levels of additive Gaussian white noise. The discriminating power of the texture descriptors is assessed using tenfolded cross-validation of a nearest neighbor classifier. The results show that several of the descriptors perform well at low levels of noise while they all suffer, to different degrees, from higher levels of introduced noise. In our tests, ILTP and FLBP show an overall good performance on several datasets. The GF are often very noise robust compared to the LBP-family under moderate to high levels of noise but not necessarily the best descriptor under low levels of added noise. In our tests, MBP is neither a good texture descriptor nor stable to noise.

  • 118.
    Kylberg, Gustaf
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Exploring Filter Banks Based on Orthogonal Moments for Texture RecognitionManuskript (preprint) (Annet vitenskapelig)
  • 119.
    Kylberg, Gustaf
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Uppström, Mats
    Hedlund, Kjell-Olof
    Borgefors, Gunilla
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Segmentation of virus particle candidates in transmission electron microscopy images2012Inngår i: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 245, nr 2, s. 140-147Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this paper, we present an automatic segmentation method that detects virus particles of various shapes in transmission electron microscopy images. The method is based on a statistical analysis of local neighbourhoods of all the pixels in the image followed by an object width discrimination and finally, for elongated objects, a border refinement step. It requires only one input parameter, the approximate width of the virus particles searched for. The proposed method is evaluated on a large number of viruses. It successfully segments viruses regardless of shape, from polyhedral to highly pleomorphic.

  • 120.
    Kårsnäs, Andreas
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Image Analysis Methods and Tools for Digital Histopathology Applications Relevant to Breast Cancer Diagnosis2014Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    In 2012, more than 1.6 million new cases of breast cancer were diagnosed and about half a million women died of breast cancer. The incidence has increased in the developing world. The mortality, however, has decreased. This is thought to partly be the result of advances in diagnosis and treatment. Studying tissue samples from biopsies through a microscope is an important part of diagnosing breast cancer. Recent techniques include camera-equipped microscopes and whole slide scanning systems that allow for digital high-throughput scanning of tissue samples. The introduction of digital pathology has simplified parts of the analysis, but manual interpretation of tissue slides is still labor intensive and costly, and involves the risk for human errors and inconsistency. Digital image analysis has been proposed as an alternative approach that can assist the pathologist in making an accurate diagnosis by providing additional automatic, fast and reproducible analyses. This thesis addresses the automation of conventional analyses of tissue, stained for biomarkers specific for the diagnosis of breast cancer, with the purpose of complementing the role of the pathologist. In order to quantify biomarker expression, extraction and classification of sub-cellular structures are needed. This thesis presents a method that allows for robust and fast segmentation of cell nuclei meeting the need for methods that are accurate despite large biological variations and variations in staining. The method is inspired by sparse coding and is based on dictionaries of local image patches. It is implemented in a tool for quantifying biomarker expression of various sub-cellular structures in whole slide images. Also presented are two methods for classifying the sub-cellular localization of staining patterns, in an attempt to automate the validation of antibody specificity, an important task within the process of antibody generation.  In addition, this thesis explores methods for evaluation of multimodal data. Algorithms for registering consecutive tissue sections stained for different biomarkers are evaluated, both in terms of registration accuracy and deformation of local structures. A novel region-growing segmentation method for multimodal data is also presented. In conclusion, this thesis presents computerized image analysis methods and tools of potential value for digital pathology applications.

    Delarbeid
    1. Learning histopathological patterns
    Åpne denne publikasjonen i ny fane eller vindu >>Learning histopathological patterns
    2012 (engelsk)Inngår i: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 2, s. 12-Artikkel i tidsskrift (Fagfellevurdert) Published
    Abstract [en]

    Aims: The aim was to demonstrate a method for automated image analysis of immunohistochemically stained tissue samples for extracting features that correlate with patient disease. We address the problem of quantifying tumor tissue and segmenting and counting cell nuclei. Materials and Methods: Our method utilizes a flexible segmentation method based on sparse coding trained from representative image samples. Nuclei counting is based on a nucleus model that takes size, shape, and nucleus probability into account. Nuclei clustering and overlays are resolved using a gray-weighted distance transform. We obtain a probability measure for pixels belonging to a nucleus from our segmentation procedure. Experiments are carried out on two sets of immunohistochemically stained images - one set based on the estrogen receptor (ER) and the other on antigen KI-67. For the nuclei separation we have selected 207 ER image samples from 58 tissue micro array-cores corresponding to 58 patients and 136 KI-67 image samples also from 58 cores. The images are hand-annotated by marking the center position of each nucleus. For the ER data we have a total of 1006 nuclei and for the KI-67 we have 796 nuclei. Segmentation performance was evaluated in terms of missing nuclei, falsely detected nuclei, and multiple detections. The proposed method is compared to state-of-the-art Bayesian classification. Statistical analysis used: The performance of the proposed method and a state-of-the-art algorithm including variations thereof is compared using the Wilcoxon rank sum test. Results: For both the ER experiment and the KI-67 experiment the proposed method exhibits lower error rates than the state-of-the-art method. Total error rates were 4.8 % and 7.7 % in the two experiments, corresponding to an average of 0.23 and 0.45 errors per image, respectively. The Wilcoxon rank sum tests show statistically significant improvements over the state-of-the-art method. Conclusions: We have demonstrated a method and obtained good performance compared to state-of-the-art nuclei separation. The segmentation procedure is simple, highly flexible, and we demonstrate how it, in addition to the nuclei separation, can perform precise segmentation of cancerous tissue. The complexity of the segmentation procedure is linear in the image size and the nuclei separation is linear in the number of nuclei. Additionally the method can be parallelized to obtain high-speed computations.

    Emneord
    Computer-aided classification, digital histopathology, flexible learning-based segmentation, image segmentation
    HSV kategori
    Forskningsprogram
    Datoriserad bildanalys; Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-167255 (URN)10.4103/2153-3539.92033 (DOI)
    Tilgjengelig fra: 2012-01-19 Laget: 2012-01-24 Sist oppdatert: 2017-12-08bibliografisk kontrollert
    2. The Vectorial Minimum Barrier Distance
    Åpne denne publikasjonen i ny fane eller vindu >>The Vectorial Minimum Barrier Distance
    2012 (engelsk)Inngår i: International Conference on Pattern Recognition, ISSN 1051-4651, s. 792-795Artikkel i tidsskrift, Meeting abstract (Fagfellevurdert) Published
    Abstract [en]

    We introduce the vectorial Minimum Barrier Distance (MBD), a method for computing a gray-weighted distance transform while also incorporating information from vectorial data. Compared to other similar tools that use vectorial data, the proposed method requires no training and does not assume having only one background class. We describe a region growing algorithm for computing the vectorial MBD efficiently.

    The method is evaluated on two types of multi-channel images: color images and textural features. Different path-cost functions for calculating the multi-dimensional path-cost distance are also compared.

    The results show that by combining multi-channel images into vectorial information the performance ofthe vectorial MBD segmentation is improved compared to when one channel is used. This implies that the method can be a good way of incorporating multi-channel information in interactive segmentation.

    HSV kategori
    Forskningsprogram
    Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-190013 (URN)978-1-4673-2216-4 (ISBN)
    Konferanse
    International Conference on Pattern Recognition, 2012
    Tilgjengelig fra: 2013-01-07 Laget: 2013-01-07 Sist oppdatert: 2014-04-29bibliografisk kontrollert
    3. Automated classification of immunostaining patterns in breast tissue from the Human Protein Atlas
    Åpne denne publikasjonen i ny fane eller vindu >>Automated classification of immunostaining patterns in breast tissue from the Human Protein Atlas
    Vise andre…
    2013 (engelsk)Inngår i: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 4, nr 14Artikkel i tidsskrift (Fagfellevurdert) Published
    Abstract [en]

    Background:

    The Human Protein Atlas (HPA) is an effort to map the location of all human proteins (http://www.proteinatlas.org/). It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA) are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples.

    Materials and Methods:

    The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM) features, complex wavelet co-occurrence matrix (CWCM) features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM)-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM) and linear discriminant analysis (LDA) classifier). Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue.

    Results:

    We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert.

    Conclusions:

    Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for quantification of staining patterns in histopathology have many applications, ranging from antibody quality control to tumor grading.

    HSV kategori
    Forskningsprogram
    Medicinsk beteendevetenskap; Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-212564 (URN)10.4103/2153-3539.109881 (DOI)
    Tilgjengelig fra: 2013-12-11 Laget: 2013-12-11 Sist oppdatert: 2017-12-06bibliografisk kontrollert
    4. A histopathological tool for quantification of biomarkers with sub-cellular resolution
    Åpne denne publikasjonen i ny fane eller vindu >>A histopathological tool for quantification of biomarkers with sub-cellular resolution
    Vise andre…
    2015 (engelsk)Inngår i: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, ISSN 2168-1163, Vol. 3, nr 1, s. 25-46Artikkel i tidsskrift (Fagfellevurdert) Published
    HSV kategori
    Forskningsprogram
    Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-219287 (URN)10.1080/21681163.2014.885120 (DOI)
    Tilgjengelig fra: 2014-02-27 Laget: 2014-02-26 Sist oppdatert: 2015-03-12bibliografisk kontrollert
    5. Multimodal histological image registration using locally rigid transforms
    Åpne denne publikasjonen i ny fane eller vindu >>Multimodal histological image registration using locally rigid transforms
    2014 (engelsk)Inngår i: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531Artikkel i tidsskrift (Annet vitenskapelig) Submitted
    Abstract [en]

    Evaluating multimodal histological images is animportant task within cancer diagnosis and research. Newmethods are currently under development, such as multiplexingand destaining/restaining protocols, but comparing data fromconsecutive monomodal sections is still the most common methodfor acquiring multimodal data. To allow for comparison of con-secutive sections, registration of the sections is needed. Becauseof the spatial distance between the sections as well as non-uniform deformations, due to mechanical and chemical stressduring handling and staining, this is not a trivial task. Inthis paper, we confirm that deformable transforms outperformlinear transforms when it comes to registration quality. However,large deformations can result in a poor viewing experience forthe pathologist when evaluating the slides, as local structuresare distorted and may look unnatural. The deformations alsoaffect measurements made on the deformed image. We presenta method for locally approximating the global deformabletransform with a rigid transform, and we introduce a gradeof rigidity term that enables a trade-off between registrationquality and measurement distortion. We use a strategy of dividingthe registration in an offline and online step, which gives usthe possibility to perform the approximation in real-time. Thisability offers the viewer with the possibility to quickly switchbetween a view that has optimal registration and a view wheremeasurements are not distorted and where structures ”lookright”. To facilitate further research within the subject, wepresent a registration tool that provides an intuitive interfacefor making comparisons between global deformable transformsand locally rigid approximations with varying degree of rigidity.

    Emneord
    Multimodal registration, digital histopathology, locally rigid transforms
    HSV kategori
    Forskningsprogram
    Datoriserad bildanalys; Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-219290 (URN)
    Tilgjengelig fra: 2014-02-26 Laget: 2014-02-26 Sist oppdatert: 2018-01-11bibliografisk kontrollert
  • 121.
    Kårsnäs, Andreas
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Dahl, Anders L.
    Technical University of Denmark, Department of Informatics and Mathematical Modelling.
    Larsen, Rasmus
    Technical University of Denmark, Department of Informatics and Mathematical Modelling.
    Learning histopathological patterns2012Inngår i: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 2, s. 12-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Aims: The aim was to demonstrate a method for automated image analysis of immunohistochemically stained tissue samples for extracting features that correlate with patient disease. We address the problem of quantifying tumor tissue and segmenting and counting cell nuclei. Materials and Methods: Our method utilizes a flexible segmentation method based on sparse coding trained from representative image samples. Nuclei counting is based on a nucleus model that takes size, shape, and nucleus probability into account. Nuclei clustering and overlays are resolved using a gray-weighted distance transform. We obtain a probability measure for pixels belonging to a nucleus from our segmentation procedure. Experiments are carried out on two sets of immunohistochemically stained images - one set based on the estrogen receptor (ER) and the other on antigen KI-67. For the nuclei separation we have selected 207 ER image samples from 58 tissue micro array-cores corresponding to 58 patients and 136 KI-67 image samples also from 58 cores. The images are hand-annotated by marking the center position of each nucleus. For the ER data we have a total of 1006 nuclei and for the KI-67 we have 796 nuclei. Segmentation performance was evaluated in terms of missing nuclei, falsely detected nuclei, and multiple detections. The proposed method is compared to state-of-the-art Bayesian classification. Statistical analysis used: The performance of the proposed method and a state-of-the-art algorithm including variations thereof is compared using the Wilcoxon rank sum test. Results: For both the ER experiment and the KI-67 experiment the proposed method exhibits lower error rates than the state-of-the-art method. Total error rates were 4.8 % and 7.7 % in the two experiments, corresponding to an average of 0.23 and 0.45 errors per image, respectively. The Wilcoxon rank sum tests show statistically significant improvements over the state-of-the-art method. Conclusions: We have demonstrated a method and obtained good performance compared to state-of-the-art nuclei separation. The segmentation procedure is simple, highly flexible, and we demonstrate how it, in addition to the nuclei separation, can perform precise segmentation of cancerous tissue. The complexity of the segmentation procedure is linear in the image size and the nuclei separation is linear in the number of nuclei. Additionally the method can be parallelized to obtain high-speed computations.

  • 122.
    Kårsnäs, Andreas
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Strand, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Multimodal histological image registration using locally rigid transforms2015Inngår i: Proc. Interactive Medical Image Computing Workshop, 2015Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Evaluating multimodal histological images is an important task within cancer diagnosis. Using aligned consecutive sections is still the most straight-forward approach for combining multimodal data.

    To overcome the difficulties in aligning the sections, we present an interactive registration approach and show its usage for aligning TMA core images from consecutive sections stained for different biomarkers. In order to reduce distortion of local structures, a global deformable transform is approximated with locally more or less rigid transformations. This gives a trade-off between registration quality and distortion of local structures. The method divides the registration in an offline (global registration) and online step, where the local approximation is done in real-time within current field of view. This approach gives the viewer the ability to quickly adjust the rigidity from a deformable, well-aligned transformation to a rigid where structures "look right''.

  • 123.
    Kårsnäs, Andreas
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Strand, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Doré, Johan
    Ebstrup, Thomas
    Lippert, Michael
    Bjerrum, Kim
    A histopathological tool for quantification of biomarkers with sub-cellular resolution2015Inngår i: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, ISSN 2168-1163, Vol. 3, nr 1, s. 25-46Artikkel i tidsskrift (Fagfellevurdert)
  • 124.
    Kårsnäs, Andreas
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Strand, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Saha, Punam K.
    Department of Electrical and Computer Engineering and the Department of Radiology, The University of Iowa, Iowa City, IA 52242 USA.
    The Vectorial Minimum Barrier Distance2012Inngår i: International Conference on Pattern Recognition, ISSN 1051-4651, s. 792-795Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We introduce the vectorial Minimum Barrier Distance (MBD), a method for computing a gray-weighted distance transform while also incorporating information from vectorial data. Compared to other similar tools that use vectorial data, the proposed method requires no training and does not assume having only one background class. We describe a region growing algorithm for computing the vectorial MBD efficiently.

    The method is evaluated on two types of multi-channel images: color images and textural features. Different path-cost functions for calculating the multi-dimensional path-cost distance are also compared.

    The results show that by combining multi-channel images into vectorial information the performance ofthe vectorial MBD segmentation is improved compared to when one channel is used. This implies that the method can be a good way of incorporating multi-channel information in interactive segmentation.

  • 125.
    Lampinen, Björn
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi.
    Protocol optimization of the filter exchange imaging (FEXI) sequence and implications on group sizes: a test-retest study2012Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    Diffusion weighted imaging (DWI) is a branch within the field of magnetic resonance imaging (MRI) that relies on the diffusion of water molecules for its contrast. Its clinical applications include the early diagnosis of ischemic stroke and mapping of the nerve tracts of the brain. The recent development of filter exchange imaging (FEXI) and the introduction of the apparent exchange rate (AXR) present a new DWI based technique that uses the exchange of water between compartments as contrast. FEXI could offer new clinical possibilities in diagnosis, differentiation and treatment follow-up of conditions involving edema or altered membrane permeability, such as tumors, cerebral edema, multiple sclerosis and stroke. Necessary steps in determining the potential of AXR as a new biomarker include running comparative studies between controls and different patient groups, looking for conditions showing large AXR-changes. However, before designing such studies, the experimental protocol of FEXI should be optimized to minimize the experimental variance. Such optimization would improve the data quality, shorten the scan time and keep the required study group sizes smaller. 

    Here, optimization was done using an active imaging approach and the Cramer-Rao lower bound (CRLB) of Fisher information theory. Three optimal protocols were obtained, each specialized at different tissue types, and the CRLB method was verified by bootstrapping. A test-retest study of 18 volunteers was conducted in order to investigate the reproducibility of the AXR as measured by one of the protocols, adapted for the scanner. Group sizes required were calculated based on both CRLB and the variability of the test-retest data, as well as choices in data analysis such as region of interest (ROI) size.

    The result of this study is new protocols offering a reduction in coefficient of variation (CV) of around 30%, as compared to previously presented protocols. Calculations of group sizes required showed that they can be used to decide whether any patient group, in a given brain region, has large alterations of AXR using as few as four individuals per group, on average, while still keeping the scan time below 15 minutes. The test-retest study showed a larger than expected variability however, and uncovered artifact like changes in AXR between measurements. Reproducibility of AXR values ranged from modest to acceptable, depending on the brain region. Group size estimations based on the collected data showed that it is still possible to detect AXR difference larger than 50% in most brain regions using fewer than ten individuals.

    Limitations of this study include an imprecise knowledge of model priors and a possibly suboptimal modeling of the bias caused by weak signals. Future studies on FEXI methodology could improve the method further by addressing these matters and possibly also the unknown source of variability. For minimal variability, comparative studies of AXR in patient groups could use a protocol among those presented here, while choosing large ROI sizes and calculating the AXR based on averaged signals.

  • 126. Langer, Max
    et al.
    Cloetens, Peter
    Hesse, Bernhard
    Suhonen, Heikki
    Pacureanu, Alexandra
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Raum, Kay
    Peyrin, Françoise
    Priors for X-ray in-line phase tomography of heterogeneous objects2014Inngår i: Philosophical Transactions. Series A: Mathematical, physical, and engineering science, ISSN 1364-503X, E-ISSN 1471-2962, Vol. 372, nr 2010, s. 20130129:1-9Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We present a new prior for phase retrieval from X-ray Fresnel diffraction patterns. Fresnel diffraction patterns are achieved by letting a highly coherent X-ray beam propagate in free space after interaction with an object. Previously, either homogeneous or multi-material object assumptions have been used. The advantage of the homogeneous object assumption is that the prior can be introduced in the Radon domain. Heterogeneous object priors, on the other hand, have to be applied in the object domain. Here, we let the relationship between attenuation and refractive index vary as a function of the measured attenuation index. The method is evaluated using images acquired at beamline ID19 (ESRF, Grenoble, France) of a phantom where the prior is calculated by linear interpolation and of a healing bone obtained from a rat osteotomy model. It is shown that the ratio between attenuation and refractive index in bone for different levels of mineralization follows a power law. Reconstruction was performed using the mixed approach but is compatible with other, more advanced models. We achieve more precise reconstructions than previously reported in literature. We believe that the proposed method will find application in biomedical imaging problems where the object is strongly heterogeneous, such as bone healing and biomaterials engineering.

  • 127.
    Lannsjö, Marianne
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för neurovetenskap, Rehabiliteringsmedicin. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Centrum för klinisk forskning, Gävleborg.
    Raininko, Raili
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för radiologi, onkologi och strålningsvetenskap, Enheten för radiologi.
    Bustamante, Mariana
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    von Seth, Charlotta
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för neurovetenskap, Rehabiliteringsmedicin.
    Borg, Jörgen
    Brain pathology after mild traumatic brain injury: An exploratory study by repeated magnetic resonance examination2013Inngår i: Journal of Rehabilitation Medicine, ISSN 1650-1977, E-ISSN 1651-2081, Vol. 45, nr 8, s. 721-728Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Objective:

    To explore brain pathology after mild traumatic brain injury by repeated magnetic resonance examination.

    Design:

    A prospective follow-up study.

    Subjects:

    Nineteen patients with mild traumatic brain injury presenting with Glasgow Coma Scale (GCS) 14-15.

    Methods:

    The patients were examined on day 2 or 3 and 3-7 months after the injury. The magnetic resonance protocol comprised conventional T1- and T2-weighted sequences including fluid attenuated inversion recovery (FLAIR), two susceptibility-weighted sequences to reveal haemorrhages, and diffusion-weighted sequences. Computer-aided volume comparison was performed. Clinical outcome was assessed by the Rivermead Post-Concussion Symptoms Questionnaire (RPQ), Hospital Anxiety and Depression Scale (HADS) and Glasgow Outcome Scale Extended (GOSE).

    Results:

    At follow-up, 7 patients (37%) reported ≥  3 symptoms in RPQ, 5 reported some anxiety and 1 reported mild depression. Fifteen patients reported upper level of good recovery and 4 patients lower level of good recovery (GOSE 8 and 7, respectively). Magnetic resonance pathology was found in 1 patient at the first examination, but 4 patients (21%) showed volume loss at the second examination, at which 3 of them reported < 3 symptoms and 1 ≥ 3 symptoms, all exhibiting GOSE scores of 8.

    Conclusion:

    Loss of brain volume, demonstrated by computer-aided magnetic resonance imaging volumetry, may be a feasible marker of brain pathology after mild traumatic brain injury.

  • 128.
    Lebre, Marie-Ange
    et al.
    Univ Clermont Auvergne, CHU Clermont Ferrand, CNRS, SIGMA Clermont,Inst Pascal, F-63000 Clermont Ferrand, France.
    Vacavant, Antoine
    Univ Clermont Auvergne, CHU Clermont Ferrand, CNRS, SIGMA Clermont,Inst Pascal, F-63000 Clermont Ferrand, France.
    Grand-Brochier, Manuel
    Univ Clermont Auvergne, CHU Clermont Ferrand, CNRS, SIGMA Clermont,Inst Pascal, F-63000 Clermont Ferrand, France.
    Rositi, Hugo
    Univ Clermont Auvergne, CHU Clermont Ferrand, CNRS, SIGMA Clermont,Inst Pascal, F-63000 Clermont Ferrand, France.
    Strand, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Rosier, Hubert
    Ctr Hosp Emile Roux, Le Puy En Velay, France.
    Abergel, Armand
    Univ Clermont Auvergne, CHU Clermont Ferrand, CNRS, SIGMA Clermont,Inst Pascal, F-63000 Clermont Ferrand, France.
    Chabrot, Pascal
    Univ Clermont Auvergne, CHU Clermont Ferrand, CNRS, SIGMA Clermont,Inst Pascal, F-63000 Clermont Ferrand, France.
    Magnin, Benoit
    Univ Clermont Auvergne, CHU Clermont Ferrand, CNRS, SIGMA Clermont,Inst Pascal, F-63000 Clermont Ferrand, France.
    A robust multi-variability model based liver segmentation algorithm for CT-scan and MRI modalities2019Inngår i: Computerized Medical Imaging and Graphics, ISSN 0895-6111, E-ISSN 1879-0771, Vol. 76, artikkel-id UNSP 101635Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Developing methods to segment the liver in medical images, study and analyze it remains a significant challenge. The shape of the liver can vary considerably from one patient to another, and adjacent organs are visualized in medical images with similar intensities, making the boundaries of the liver ambiguous. Consequently, automatic or semi-automatic segmentation of liver is a difficult task. Moreover, scanning systems and magnetic resonance imaging have different settings and parameters. Thus the images obtained differ from one machine to another. In this article, we propose an automatic model-based segmentation that allows building a faithful 3-D representation of the liver, with a mean Dice value equal to 90.3% on CT and MRI datasets. We compare our algorithm with a semi-automatic method and with other approaches according to the state of the art. Our method works with different data sources, we use a large quantity of CT and MRI images from machines in various hospitals and multiple DICOM images available from public challenges. Finally, for evaluation of liver segmentation approaches in state of the art, robustness is not adequacy addressed with a precise definition. Another originality of this article is the introduction of a novel measure of robustness, which takes into account the liver variability at different scales. (C) 2019 Published by Elsevier Ltd.

  • 129.
    Lee, Doojin
    et al.
    Gwangju Inst Sci & Technol, Dept Biomed Sci & Engn, Gwangju, South Korea.
    Kim, Kangwook
    Gwangju Inst Sci & Technol, Dept Biomed Sci & Engn, Gwangju, South Korea.
    Augustine, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Tekniska sektionen, Institutionen för teknikvetenskaper, Fasta tillståndets elektronik.
    Monitoring of the Skull Healing within Layered Head Model Based on Transmission Line Theory2017Inngår i: 2017 1st IEEE MTT-S International Microwave Bio Conference (IMBioC), 2017Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The possibility of monitoring the skull healing after craniotomy using microwaves is studied. The layered head model is constructed using transmission line. The reflected pulse for each layer can then be obtained and is used to monitor the skull healing. The dispersive dielectric profiles for each layer are considered. The simulation results show that the shifts from reflected pulse at skull-brain interface can be monitored as the skull thickness is varied from 0mm to 10mm.

  • 130.
    Lee, Doojin
    et al.
    Univ Waterloo, Mech & Mechatron Engn, Waterloo, ON, Canada.
    Shaker, George
    Univ Waterloo, Mech & Mechatron Engn, Waterloo, ON, Canada.
    Nowinski, Daniel
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Plastikkirurgi.
    Augustine, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Tekniska sektionen, Institutionen för teknikvetenskaper, Fasta tillståndets elektronik.
    Monitoring of Healing Progression of Cranial Vault using One-dimensional Pulsed Radar Technique2018Inngår i: Proceedings of the 2018 IEEE/MTT-S International Microwave Biomedical Conference (IMBioC), IEEE, 2018, s. 64-66Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper, the skull healing after surgery has been investigated using proposed resistively loaded antenna utilizing the principles of short pulse radar technique. The one-dimensional pulsed profile for every stage has been demonstrated that the healing stages after craniotomy can be monitored by observing the change in the amplitude of the matched filter responses.

  • 131.
    Lidayová, Kristína
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Fast Methods for Vascular Segmentation Based on Approximate Skeleton Detection2017Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Modern medical imaging techniques have revolutionized health care over the last decades, providing clinicians with high-resolution 3D images of the inside of the patient's body without the need for invasive procedures. Detailed images of the vascular anatomy can be captured by angiography, providing a valuable source of information when deciding whether a vascular intervention is needed, for planning treatment, and for analyzing the success of therapy. However, increasing level of detail in the images, together with a wide availability of imaging devices, lead to an urgent need for automated techniques for image segmentation and analysis in order to assist the clinicians in performing a fast and accurate examination.

    To reduce the need for user interaction and increase the speed of vascular segmentation,  we propose a fast and fully automatic vascular skeleton extraction algorithm. This algorithm first analyzes the volume's intensity histogram in order to automatically adapt the internal parameters to each patient and then it produces an approximate skeleton of the patient's vasculature. The skeleton can serve as a seed region for subsequent surface extraction algorithms. Further improvements of the skeleton extraction algorithm include the expansion to detect the skeleton of diseased arteries and the design of a convolutional neural network classifier that reduces false positive detections of vascular cross-sections. In addition to the complete skeleton extraction algorithm, the thesis presents a segmentation algorithm based on modified onion-kernel region growing. It initiates the growing from the previously extracted skeleton and provides a rapid binary segmentation of tubular structures. To provide the possibility of extracting precise measurements from this segmentation we introduce a method for obtaining a segmentation with subpixel precision out of the binary segmentation and the original image. This method is especially suited for thin and elongated structures, such as vessels, since it does not shrink the long protrusions. The method supports both 2D and 3D image data.

    The methods were validated on real computed tomography datasets and are primarily intended for applications in vascular segmentation, however, they are robust enough to work with other anatomical tree structures after adequate parameter adjustment, which was demonstrated on an airway-tree segmentation.

    Delarbeid
    1. Fast vascular skeleton extraction algorithm
    Åpne denne publikasjonen i ny fane eller vindu >>Fast vascular skeleton extraction algorithm
    Vise andre…
    2016 (engelsk)Inngår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 76, s. 67-75Artikkel i tidsskrift (Fagfellevurdert) Published
    HSV kategori
    Forskningsprogram
    Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-267146 (URN)10.1016/j.patrec.2015.06.024 (DOI)000375135600009 ()
    Tilgjengelig fra: 2015-07-09 Laget: 2015-11-18 Sist oppdatert: 2017-12-01bibliografisk kontrollert
    2. Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation
    Åpne denne publikasjonen i ny fane eller vindu >>Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation
    2017 (engelsk)Inngår i: Journal of Medical Imaging, ISSN 2329-4302, E-ISSN 2329-4310, Vol. 4, s. 024004:1-11Artikkel i tidsskrift (Fagfellevurdert) Published
    HSV kategori
    Forskningsprogram
    Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-318527 (URN)10.1117/1.JMI.4.2.024004 (DOI)000405944600018 ()28466028 (PubMedID)
    Tilgjengelig fra: 2017-04-28 Laget: 2017-03-28 Sist oppdatert: 2017-11-17bibliografisk kontrollert
    3. Airway-tree segmentation in subjects with acute respiratory distress syndrome
    Åpne denne publikasjonen i ny fane eller vindu >>Airway-tree segmentation in subjects with acute respiratory distress syndrome
    Vise andre…
    2017 (engelsk)Inngår i: Image Analysis: Part II, Springer, 2017, s. 76-87Konferansepaper, Publicerat paper (Fagfellevurdert)
    sted, utgiver, år, opplag, sider
    Springer, 2017
    Serie
    Lecture Notes in Computer Science ; 10270
    HSV kategori
    Forskningsprogram
    Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-318528 (URN)10.1007/978-3-319-59129-2_7 (DOI)000454360300007 ()978-3-319-59128-5 (ISBN)
    Konferanse
    SCIA 2017, June 12–14, Tromsø, Norway
    Forskningsfinansiär
    Swedish Research Council, 621-2014-6153
    Tilgjengelig fra: 2017-05-19 Laget: 2017-03-29 Sist oppdatert: 2019-02-27bibliografisk kontrollert
    4. Coverage segmentation of thin structures by linear unmixing and local centre of gravity attraction
    Åpne denne publikasjonen i ny fane eller vindu >>Coverage segmentation of thin structures by linear unmixing and local centre of gravity attraction
    2013 (engelsk)Inngår i: Proc. 8th International Symposium on Image and Signal Processing and Analysis, IEEE Signal Processing Society, 2013, s. 83-88Konferansepaper, Publicerat paper (Fagfellevurdert)
    sted, utgiver, år, opplag, sider
    IEEE Signal Processing Society, 2013
    HSV kategori
    Forskningsprogram
    Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-211532 (URN)10.1109/ISPA.2013.6703719 (DOI)000349789200016 ()978-953-184-194-8 (ISBN)
    Konferanse
    ISPA 2013, September 4–6, Trieste, Italy
    Forskningsfinansiär
    Swedish Research Council, 2011-5197
    Tilgjengelig fra: 2014-01-10 Laget: 2013-11-26 Sist oppdatert: 2018-08-24bibliografisk kontrollert
    5. Coverage segmentation of 3D thin structures
    Åpne denne publikasjonen i ny fane eller vindu >>Coverage segmentation of 3D thin structures
    Vise andre…
    2015 (engelsk)Inngår i: Proc. 5th International Conference on Image Processing Theory, Tools and Applications, Piscataway, NJ: IEEE , 2015, s. 23-28Konferansepaper, Publicerat paper (Fagfellevurdert)
    sted, utgiver, år, opplag, sider
    Piscataway, NJ: IEEE, 2015
    HSV kategori
    Forskningsprogram
    Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-267161 (URN)10.1109/IPTA.2015.7367089 (DOI)000380472700002 ()978-1-4799-8636-1 (ISBN)
    Konferanse
    IPTA 2015, November 10–13, Orléans, France
    Forskningsfinansiär
    Swedish Research Council, 621-2014-6153
    Tilgjengelig fra: 2016-01-12 Laget: 2015-11-18 Sist oppdatert: 2018-08-24bibliografisk kontrollert
    6. Classification of cross-sections for vascular skeleton extraction using convolutional neural networks
    Åpne denne publikasjonen i ny fane eller vindu >>Classification of cross-sections for vascular skeleton extraction using convolutional neural networks
    Vise andre…
    2017 (engelsk)Inngår i: Medical Image Understanding and Analysis, Springer, 2017, s. 182-194Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract
    sted, utgiver, år, opplag, sider
    Springer, 2017
    Serie
    Communications in Computer and Information Science ; 723
    HSV kategori
    Forskningsprogram
    Datoriserad bildbehandling
    Identifikatorer
    urn:nbn:se:uu:diva-318529 (URN)10.1007/978-3-319-60964-5_16 (DOI)978-3-319-60963-8 (ISBN)
    Konferanse
    MIUA 2017, July 11–13, Edinburgh, UK
    Tilgjengelig fra: 2017-06-22 Laget: 2017-03-24 Sist oppdatert: 2018-07-03bibliografisk kontrollert
  • 132.
    Lidayová, Kristína
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Frimmel, Hans
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för beräkningsvetenskap.
    Bengtsson, Ewert
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Smedby, Örjan
    Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation2017Inngår i: Journal of Medical Imaging, ISSN 2329-4302, E-ISSN 2329-4310, Vol. 4, s. 024004:1-11Artikkel i tidsskrift (Fagfellevurdert)
  • 133.
    Lidayová, Kristína
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Frimmel, Hans
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för beräkningsvetenskap.
    Wang, Chunliang
    Bengtsson, Ewert
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Smedby, Örjan
    Fast vascular skeleton extraction algorithm2016Inngår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 76, s. 67-75Artikkel i tidsskrift (Fagfellevurdert)
  • 134.
    Lidayová, Kristína
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Gupta, Anindya
    Frimmel, Hans
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för beräkningsvetenskap.
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Bengtsson, Ewert
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Smedby, Örjan
    Classification of cross-sections for vascular skeleton extraction using convolutional neural networks2017Inngår i: Medical Image Understanding and Analysis, Springer, 2017, s. 182-194Konferansepaper (Fagfellevurdert)
    Abstract
  • 135.
    Lidayová, Kristína
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Gómez Betancur, Duván Alberto
    Frimmel, Hans
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för beräkningsvetenskap.
    Hernández Hoyos, Marcela
    Orkisz, Maciej
    Smedby, Örjan
    Airway-tree segmentation in subjects with acute respiratory distress syndrome2017Inngår i: Image Analysis: Part II, Springer, 2017, s. 76-87Konferansepaper (Fagfellevurdert)
  • 136.
    Lidayová, Kristína
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Lindblad, Joakim
    Sladoje, Nataša
    Frimmel, Hans
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för beräkningsvetenskap.
    Coverage segmentation of thin structures by linear unmixing and local centre of gravity attraction2013Inngår i: Proc. 8th International Symposium on Image and Signal Processing and Analysis, IEEE Signal Processing Society, 2013, s. 83-88Konferansepaper (Fagfellevurdert)
  • 137.
    Lidayová, Kristína
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Lindblad, Joakim
    Sladoje, Nataša
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Frimmel, Hans
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för beräkningsvetenskap.
    Wang, Chunliang
    Smedby, Örjan
    Coverage segmentation of 3D thin structures2015Inngår i: Proc. 5th International Conference on Image Processing Theory, Tools and Applications, Piscataway, NJ: IEEE , 2015, s. 23-28Konferansepaper (Fagfellevurdert)
  • 138.
    Lind, Lars
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Klinisk epidemiologi.
    Strand, Robin
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi.
    Michaëlsson, Karl
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Ortopedi.
    Kullberg, Joel
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi. Antaros Med AB, BioVenture Hub, Mölndal, Sweden.
    Ahlström, Håkan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi. Antaros Med AB, BioVenture Hub, Mölndal, Sweden.
    Relationship between endothelium-dependent vasodilation and fat distribution using the new "imiomics" image analysis technique2019Inngår i: NMCD. Nutrition Metabolism and Cardiovascular Diseases, ISSN 0939-4753, E-ISSN 1590-3729, Vol. 29, nr 10, s. 1077-1086Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background and aims: We investigated how vasoreactivity in the brachial artery and the forearm resistance vessels were related to fat distribution and tissue volume, using both traditional imaging analysis and a new technique, called “Imiomics”, whereby vasoreactivity was related to each of the >2M 3D image elements included in the whole-body magnetic resonance imaging (MRI).

    Methods and results: In 326 subjects in the Prospective investigation of Obesity, Energy and Metabolism (POEM) study (all aged 50 years), endothelium-dependent vasodilation was measured by acetylcholine infusion in the brachial artery (EDV) and flow-mediated vasodilation (FMD). Fat distribution was evaluated by dual-energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI). EDV, but not FMD, was significantly related to total fat mass, liver fat, subcutaneous (SAT) and visceral (VAT) adipose tissue in a negative fashion in women, but not in men. Using Imiomics, an inverse relationship was seen between EDV and a local tissue volume of SAT in both the upper part of the body, as well as the gluteo-femoral part and the medial parts of the legs in women. Also the size of the liver, heart and VAT was inversely related to EDV. In men, less pronounced relationships were seen. FMD was also significantly related to local tissue volume of upper-body SAT and liver fat in women, but less so in men.

    Conclusion: EDV, and to a lesser degree also FMD, were related to liver fat, SAT and VAT in women, but less so in men. Imiomics both confirmed findings from traditional methods and resulted in new, more detailed results.

  • 139. Lindblad, Joakim
    et al.
    Bengtsson, Ewert
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Sladoje, Nataša
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Microscopy image enhancement for cost-effective cervical cancer screening2015Inngår i: Image Analysis, Springer, 2015, s. 440-451Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We propose a simple and fast method for microscopy imageenhancement and quantitatively evaluate its performance on a databasecontaining cell images obtained from microscope setups of several levelsof quality. The method utilizes an efficiently and accurately estimated rel-ative modulation transfer function to generate images of higher quality,starting from those of lower quality, by filtering in the Fourier domain.We evaluate the method visually and based on correlation coefficientand normalized mutual information. We conclude that enhanced imagesexhibit high similarity, both visually and in terms of information con-tent, with acquired high quality images. This is an important result forthe development of a cost-effective screening system for cervical cancer.

  • 140. Lindblad, Joakim
    et al.
    Sladoje, Natasa
    Malm, Patrik
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Bengtsson, Ewert
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Moshavegh, Ramin
    Mehnert, Andrew
    Optimizing optics and imaging for pattern recognition based screening tasks2014Inngår i: Proc. 22nd International Conference on Pattern Recognition, IEEE Computer Society, 2014, s. 3333-3338Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We present a method for simulating lower quality images starting from higher quality ones, based on acquired image pairs from different optical setups. The method does not require estimates of point (or line) spread functions of the system, but utilizes the relative transfer function derived from images of real specimen of interest in the observed application. Thanks to the use of a larger number of real specimen, excellent stability and robustness of the method is achieved. The intended use is exploring the influence of image quality on features and classification accuracy in pattern recognition based screening tasks. Visual evaluation of the obtained images strongly confirms usefulness of the method. The approach is quantitatively evaluated by observing stability of feature values, proven useful for PAP-smear classification, between synthetic and real images from seven different microscope setups. The evaluation shows that features from the synthetically generated lower resolution images are as similar to features from real images at that resolution, as features from two different images of the same specimen, taken at the same low resolution, are to each other.

  • 141.
    Lindblad, Joakim
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion.
    Sladoje, Natasa
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion.
    Suveer, Amit
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Dragomir, Anca
    Sintorn, Ida-Maria
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    High-resolution reconstruction by feature distance minimization from multiple views of an object2015Inngår i: Proc. 5th International Conference on Image Processing Theory, Tools and Applications, Piscataway, NJ: IEEE , 2015, s. 29-34Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We present a method which utilizes advantages of fuzzy object representations and image processing techniques adjusted to them, to further increase efficient utilization of image information. Starting from a number of low-resolution images of affine transformations of an object, we create its suitably defuzzified high-resolution reconstruction. We evaluate the proposed method on synthetic data, observing its performance w.r.t. noise sensitivity, influence of the number of used low-resolution images, sensitivity to object variation and to inaccurate registration. Our aim is to explore applicability of the method to real image data acquired by Transmission Electron Microscopy, in a biomedical application we are currently working on.

  • 142.
    Lindmark, Sofia
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion.
    Cell Tracking in Microscopy Images Using a Rao-Blackwellized Particle Filter2014Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
    Abstract [en]

    Analysing migrating cells in microscopy time-lapse images has already helped the understanding of many biological processes and may be of importance in the development of new medical treatments. Today’s biological experiments tend to produce a huge amount of dynamic image data and tracking the individual cells by hand has become a bottleneck for the further analysis work. A number of cell tracking methods have therefore been developed over the past decades, but still many of the techniques have a limited performance.

    The aim of this Master Project is to develop a particle filter algorithm that automatically detects and tracks a large number of individual cells in an image sequence. The solution is based on a Rao-Blackwellized particle filter for multiple object tracking. The report also covers a review of existing automatic cell tracking techniques, a review of well-known filter techniques for single target tracking and how these techniques have been developed to handle multiple target tracking. The designed algorithm has been tested on real microscopy image data of neutrophils with 400 to 500 cells in each frame. The designed algorithm works well in areas of the images where no cells touch and can in these situations also correct for some segmentation mistakes. In areas where cells touch, the algorithm works well if the segmentation is correct, but often makes mistakes when it is not. A target effectiveness of 77 percent and a track purity of 80 percent are then achieved. 

  • 143.
    Lindström, Elin
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi. Department of Medical Physics, Uppsala University Hospital, Uppsala, Sweden.
    Sundin, Anders
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi.
    Trampal, Carlos
    Lindsjö, Lars
    Ilan, Ezgi
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi. Department of Medical Physics, Uppsala University Hospital, Uppsala, Sweden .
    Danfors, Torsten
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi.
    Antoni, Gunnar
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för läkemedelskemi, Preparativ läkemedelskemi.
    Sörensen, Jens
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi. PET Centre, Uppsala University Hospital, Uppsala, Sweden.
    Lubberink, Mark
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi. Department of Medical Physics, Uppsala University Hospital, Uppsala, Sweden .
    Evaluation of penalized likelihood estimation reconstruction on a digital time-of-flight PET/CT scanner for 18F-FDG whole-body examinations2018Inngår i: Journal of Nuclear Medicine, ISSN 0161-5505, E-ISSN 1535-5667, Vol. 59, nr 7, s. 1152-1158Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The resolution and quantitative accuracy of PET are highly influenced by the reconstruction method. Penalized-likelihood estimation algorithms allow for fully convergent iterative reconstruction, generating a higher image contrast than ordered-subsets expectation maximization (OSEM) while limiting noise. In this study, a type of penalized reconstruction known as block-sequential regularized expectation maximization (BSREM) was compared with time-of-flight OSEM (TOF OSEM). Various strengths of noise penalization factor β were tested along with various acquisition durations and transaxial fields of view (FOVs) with the aim of evaluating the performance and clinical use of BSREM for 18F-FDG PET/CT, both quantitatively and in a qualitative visual evaluation. Methods: Eleven clinical whole-body 18F-FDG PET/CT examinations acquired on a digital TOF PET/CT scanner were included. The data were reconstructed using BSREM with point-spread function recovery and β-factors of 133, 267, 400, and 533—and using TOF OSEM with point-spread function—for various acquisition times per bed position and various FOVs. Noise level, signal-to-noise ratio (SNR), signal-to-background ratio (SBR), and SUV were analyzed. A masked evaluation of visual image quality, rating several aspects, was performed by 2 nuclear medicine physicians to complement the analysis. Results: The lowest levels of noise were reached with the highest β-factor, resulting in the highest SNR, which in turn resulted in the lowest SBR. A β-factor of 400 gave noise equivalent to TOF OSEM but produced a significant increase in SUVmax (11%), SNR (22%), and SBR (12%). BSREM with a β-factor of 533 at a decreased acquisition duration (2 min/bed position) was comparable to TOF OSEM at a full acquisition duration (3 min/bed position). Reconstructed FOV had an impact on BSREM outcome measures; SNR increased and SBR decreased when FOV was shifted from 70 to 50 cm. The evaluation of visual image quality resulted in similar scores for reconstructions, although a β-factor of 400 obtained the highest mean whereas a β-factor of 267 was ranked best in overall image quality, contrast, sharpness, and tumor detectability. Conclusion: In comparison with TOF OSEM, penalized BSREM reconstruction resulted in an increased tumor SUVmax and an improved SNR and SBR at a matched level of noise. BSREM allowed for a shorter acquisition than TOF OSEM, with equal image quality.

  • 144.
    Lindström, Elin
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi. Uppsala Univ Hosp, Med Phys, Uppsala, Sweden.
    Velikyan, Irina
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för läkemedelskemi, Preparativ läkemedelskemi.
    Regula, Naresh Kumar
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi.
    Alhuseinalkhudhur, Ali
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi.
    Sundin, Anders
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi.
    Sörensen, Jens
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi. Uppsala Univ Hosp, PET Ctr, Uppsala, Sweden.
    Lubberink, Mark
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi. Uppsala Univ Hosp, Med Phys, Uppsala, Sweden.
    Regularized reconstruction of digital time-of-flight Ga-68-PSMA-11 PET/CT for the detection of recurrent disease in prostate cancer patients2019Inngår i: Theranostics, ISSN 1838-7640, E-ISSN 1838-7640, Vol. 9, nr 12, s. 3476-3484Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Accurate localization of recurrent prostate cancer (PCa) is critical, especially if curative therapy is intended. With the aim to optimize target-to-background uptake ratio in Ga-68-PSMA-11 PET, we investigated the image quality and quantitative measures of regularized reconstruction by block-sequential regularized expectation maximization (BSREM).

    Methods:

    The study encompassed retrospective reconstruction and analysis of 20 digital time-of-flight (TOF) PET/CT examinations acquired 60 min post injection of 2 MBq/kg of Ga-68-PSMA-11 in PCa patients with biochemical relapse after primary treatment. Reconstruction by ordered-subsets expectation maximization (OSEM; 3 iterations, 16 subsets, 5 mm gaussian postprocessing filter) and BSREM (beta-values of 100-1600) were used, both including TOF and point spread function (PSF) recovery. Background variability (BV) was measured by placing a spherical volume of interest in the right liver lobe and defined as the standard deviation divided by the mean standardized uptake value (SUV). The image quality was evaluated in terms of signal-to-noise ratio (SNR) and signal-to-background ratio (SBR), using SUVmax of the lesions. A visual assessment was performed by four observers.

    Results:

    OSEM reconstruction produced images with a BV of 15%, whereas BSREM with a beta-value above 300 resulted in lower BVs than OSEM (36% with beta 100, 8% with beta 1300). Decreasing the acquisition duration from 2 to 1 and 0.5 min per bed position increased BV for both reconstruction methods, although BSREM with beta-values equal to or higher than 800 and 1200, respectively, kept the BV below 15%. In comparison of BSREM with OSEM, the mean SNR improved by 25 to 66% with an increasing beta-value in the range of 200-1300, whereas the mean SBR decreased with an increasing beta-value, ranging from 0 to 125% with a beta-value of 100 and 900, respectively. Decreased acquisition duration resulted in beta-values of 800 to 1000 and 1200 to 1400 for 1 and 0.5 min per bed position, respectively, producing improved image quality measures compared with OSEM at a full acquisition duration of 2 min per bed position. The observer study showed a slight overall preference for BSREM beta 900 although the interobserver variability was high.

    Conclusion:

    BSREM image reconstruction with beta-values in the range of 400-900 resulted in lower BV and similar or improved SNR and SBR in comparison with OSEM.

  • 145.
    Linner, Elisabeth
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Strand, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Comparison of Restoration Quality on Square and Hexagonal Grids using Normalized Convolution2012Inngår i: Proceedings of the 21st International Conference on Pattern Recognition (ICPR), 2012Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Normalized convolution can be used to restore information that has been lost from an image, such as dead pixels, using the remaining information, and ignoring the incorrect pixels. It is known that the representation quality of an image consisting of a given number of pixels depends on how these pixels are distributed. In this paper, we investigate whether the ability to restore information using normalized convolution is affected by the sampling grid of the image. We compare square and hexagonal grids, and find that, in general, more pixels can be restored in hexagonal grids.

  • 146.
    Linnér, Elisabeth
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Strand, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    A Graph-Based Implementation of the Anti-Aliased Euclidean Distance Transform2014Inngår i: Proceedings 22nd International Conference on Pattern Recognition (ICPR), 2014, 2014, s. 1025-1030Konferansepaper (Fagfellevurdert)
    Abstract [en]

    With this paper, we present an algorithm for the anti-aliased Euclidean distance transform, based on wave front propagation, that can easily be extended to images of arbitrary dimensionality and sampling lattices. We investigate the behavior and weaknesses of the algorithm, applied to synthetic two-dimensional area-sampled images, and suggest an enhancement to the original method, with complexity proportional to the number of edge elements, that may reduce the amount and relative magnitude of the errors in the transformed image by as much as a factor of 10.

  • 147.
    Linnér, Elisabeth
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Strand, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Aliasing Properties of Voxels in Three-Dimensional Sampling Lattices2012Inngår i: Large Scale Scientific Computing, 2012, s. 507-514Konferansepaper (Fagfellevurdert)
  • 148.
    Linnér, Elisabeth
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Strand, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Anti-Aliased Euclidean Distance Transform on 3D Sampling Lattices2014Inngår i: Discrete Geometry for Computer Imagery: 18th IAPR International Conference, DGCI 2014, Siena, Italy, September 10-12, 2014. Proceedings / [ed] Elena Barcucci, Andrea Frosini, Simone Rinaldi, 2014, s. 88-98Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The Euclidean distance transform (EDT) is used in many essential operations in image processing, such as basic morphology, level sets, registration and path finding. The anti-aliased Euclidean distance transform (AAEDT), previously presented for two-dimensional images, uses the gray-level information in, for example, area sampled images to calculate distances with sub-pixel precision. Here, we extend the studies of AAEDT to three dimensions, and to the Body-Centered Cubic (BCC) and Face-Centered Cubic (FCC) lattices, which are, in many respects, considered the optimal three-dimensional sampling lattices. We compare different ways of converting gray-level information to distance values, and find that the lesser directional dependencies of optimal sampling lattices lead to better approximations of the true Euclidean distance.

  • 149.
    Linnér, Elisabeth
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Strand, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Comparison of restoration quality on square and hexagonal grids using normalized convolution2012Inngår i: Proc. 21st International Conference on Pattern Recognition, 2012, s. 3046-3049Konferansepaper (Fagfellevurdert)
  • 150. Litjens, Geert
    et al.
    Toth, Robert
    van de Ven, Wendy
    Hoeks, Caroline
    Kerkstra, Sjoerd
    van Ginneken, Bram
    Vincent, Graham
    Guillard, Gwenael
    Birbeck, Neil
    Zhang, Jindang
    Strand, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Malmberg, Filip
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
    Ou, Yangming
    Davatzikos, Christos
    Kirschner, Matthias
    Jung, Florian
    Yuan, Jing
    Qiu, Wu
    Gao, Qinquan
    Edwards, Philip Eddie
    Maan, Bianca
    van der Heijden, Ferdinand
    Ghose, Soumya
    Mitra, Jhimli
    Dowling, Jason
    Barratt, Dean
    Huisman, Henkjan
    Madabhushi, Anant
    Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge2014Inngår i: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 18, nr 2, s. 359-373Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p < 0.05) and had an efficient implementation with a run time of 8 min and 3 s per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi-atlas registration, both on accuracy and computation time. Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained. All results are available online at http://promise12.grand-challenge.org/. (C) 2013 Elsevier B.V. All rights reserved.

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