uu.seUppsala University Publications
Change search
Refine search result
12 1 - 50 of 84
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Axelsson, Maria
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Chinga, Gary
    Svensson, Stina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Nygård, Per
    Malmberg, Filip
    Solheim, Olav
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Borgefors, Gunilla
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Detailed quantification of the 3D structure of newsprints in X-ray synchrotron radiation microtomography images2006In: Progress in Paper Physics Seminar, Oxford, Ohio, 2006, 2006Conference paper (Other academic)
  • 2.
    Bajic, Buda
    et al.
    Faculty of Technical Sciences, University of Novi Sad, Serbia.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia.
    Single image super-resolution reconstruction in presence of mixed Poisson-Gaussian noise2016In: 2016 SIXTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA), IEEE, 2016Conference paper (Refereed)
    Abstract [en]

    Single image super-resolution (SR) reconstructionaims to estimate a noise-free and blur-free high resolution imagefrom a single blurred and noisy lower resolution observation.Most existing SR reconstruction methods assume that noise in theimage is white Gaussian. Noise resulting from photon countingdevices, as commonly used in image acquisition, is, however,better modelled with a mixed Poisson-Gaussian distribution. Inthis study we propose a single image SR reconstruction methodbased on energy minimization for images degraded by mixedPoisson-Gaussian noise.We evaluate performance of the proposedmethod on synthetic images, for different levels of blur andnoise, and compare it with recent methods for non-Gaussiannoise. Analysis shows that the appropriate treatment of signaldependentnoise, provided by our proposed method, leads tosignificant improvement in reconstruction performance.

  • 3.
    Bajic, Buda
    et al.
    Univ Novi Sad, Fac Tech Sci, Novi Sad, Serbia.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Serbian Acad Arts & Sci, Math Inst, Belgrade, Serbia.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Serbian Acad Arts & Sci, Math Inst, Belgrade, Serbia.
    Sparsity promoting super-resolution coverage segmentation by linear unmixing in presence of blur and noise2019In: Journal of Electronic Imaging (JEI), ISSN 1017-9909, E-ISSN 1560-229X, Vol. 28, no 1, article id 013046Article in journal (Refereed)
    Abstract [en]

    We present a segmentation method that estimates the relative coverage of each pixel in a sensed image by each image component. The proposed super-resolution blur-aware model (utilizes a priori knowledge of the image blur) for linear unmixing of image intensities relies on a sparsity promoting approach expressed by two main requirements: (i) minimization of Huberized total variation, providing smooth object boundaries and noise removal, and (ii) minimization of nonedge image fuzziness, responding to an assumption that imaged objects are crisp and that fuzziness is mainly due to the imaging and digitization process. Edge fuzziness due to partial coverage is allowed, enabling subpixel precise feature estimates. The segmentation is formulated as an energy minimization problem and solved by the spectral projected gradient method, utilizing a graduated nonconvexity scheme. Quantitative and qualitative evaluation on synthetic and real multichannel images confirms good performance, particularly relevant when subpixel precision in segmentation and subsequent analysis is a requirement. (C) 2019 SPIE and IS&T

  • 4.
    Bajic, Buda
    et al.
    Faculty of Technical Sciences, University of Novi Sad, Serbia.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Serbian Acad Arts & Sci, Math Inst, Belgrade, Serbia.
    Sladoje, Nataša
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Serbian Acad Arts & Sci, Math Inst, Belgrade, Serbia.
    Blind restoration of images degraded with mixed poisson-Gaussian noise with application in transmission electron microscopy2016In: 2016 Ieee 13Th International Symposium On Biomedical Imaging (ISBI), IEEE, 2016, p. 123-127Conference paper (Refereed)
    Abstract [en]

    Noise and blur, present in images after acquisition, negatively affect their further analysis. For image enhancement when the Point Spread Function (PSF) is unknown, blind deblurring is suitable, where both the PSF and the original image are simultaneously reconstructed. In many realistic imaging conditions, noise is modelled as a mixture of Poisson (signal-dependent) and Gaussian (signal independent) noise. In this paper we propose a blind deconvolution method for images degraded by such mixed noise. The method is based on regularized energy minimization. We evaluate its performance on synthetic images, for different blur kernels and different levels of noise, and compare with non-blind restoration. We illustrate the performance of the method on Transmission Electron Microscopy images of cilia, used in clinical practice for diagnosis of a particular type of genetic disorders.

  • 5.
    Bajic, Buda
    et al.
    Faculty of Technical Sciences, University of Novi Sad, Serbia.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia.
    Sladoje, Nataša
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia.
    Restoration of images degraded by signal-dependent noise based on energy minimization: an empirical study2016In: Journal of Electronic Imaging (JEI), ISSN 1017-9909, E-ISSN 1560-229X, Vol. 25, no 4, article id 043020Article in journal (Refereed)
    Abstract [en]

    Most energy minimization-based restoration methods are developed for signal-independent Gaussian noise. The assumption of Gaussian noise distribution leads to a quadratic data fidelity term, which is appealing in optimization. When an image is acquired with a photon counting device, it contains signal-dependent Poisson or mixed Poisson–Gaussian noise. We quantify the loss in performance that occurs when a restoration method suited for Gaussian noise is utilized for mixed noise. Signal-dependent noise can be treated by methods based on either classical maximum a posteriori (MAP) probability approach or on a variance stabilization approach (VST). We compare performances of these approaches on a large image material and observe that VST-based methods outperform those based on MAP in both quality of restoration and in computational efficiency. We quantify improvement achieved by utilizing Huber regularization instead of classical total variation regularization. The conclusion from our study is a recommendation to utilize a VST-based approach combined with regularization by Huber potential for restoration of images degraded by blur and signal-dependent noise. This combination provides a robust and flexible method with good performance and high speed.

  • 6. Bajic, Buda
    et al.
    Suveer, Amit
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Gupta, Anindya
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Pepic, Ivana
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Denoising of short exposure transmission electron microscopy images for ultrastructural enhancement2018In: Proc. 15th International Symposium on Biomedical Imaging, IEEE, 2018, p. 921-925Conference paper (Refereed)
  • 7. Bajić, Buda
    et al.
    Lindblad, Joakim
    Sladoje, Nataša
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    An evaluation of potential functions for regularized image deblurring2014In: Image Analysis and Recognition: Part I, Springer Berlin/Heidelberg, 2014, p. 150-158Conference paper (Refereed)
  • 8.
    Bengtsson, Ewert
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala university.
    Wieslander, Håkan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Forslid, Gustav
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Hirsch, Jan-Michael
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Oral and Maxillofacial Surgery.
    Runow Stark, Christina
    Kecheril Sadanandan, Sajith
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Detection of Malignancy-Associated Changes Due to Precancerous and Oral Cancer Lesions: A Pilot Study Using Deep Learning2018In: CYTO2018 / [ed] Andrea Cossarizza, 2018Conference paper (Refereed)
    Abstract [en]

    Background: The incidence of oral cancer is increasing and it is effecting younger individuals. PAP smear-based screening, visual, and automated, have been used for decades, to successfully decrease the incidence of cervical cancer. Can similar methods be used for oral cancer screening? We have carried out a pilot study using neural networks for classifying cells, both from cervical cancer and oral cancer patients. The results which were reported from a technical point of view at the 2017 IEEE International Conference on Computer Vision Workshop (ICCVW), were particularly interesting for the oral cancer cases, and we are currently collecting and analyzing samples from more patients. Methods: Samples were collected with a brush in the oral cavity and smeared on glass slides, stained, and prepared, according to standard PAP procedures. Images from the slides were digitized with a 0.35 micron pixel size, using focus stacks with 15 levels 0.4 micron apart. Between 245 and 2,123 cell nuclei were manually selected for analysis for each of 14 datasets, usually 2 datasets for each of the 6 cases, in total around 15,000 cells. A small region was cropped around each nucleus, and the best 2 adjacent focus layers in each direction were automatically found, thus creating images of 100x100x5 pixels. Nuclei were chosen with an aim to select well preserved free-lying cells, with no effort to specifically select diagnostic cells. We therefore had no ground truth on the cellular level, only on the patient level. Subsets of these images were used for training 2 sets of neural networks, created according to the ResNet and VGG architectures described in literature, to distinguish between cells from healthy persons, and those with precancerous lesions. The datasets were augmented through mirroring and 90 degrees rotations. The resulting networks were used to classify subsets of cells from different persons, than those in the training sets. This was repeated for a total of 5 folds. Results: The results were expressed as the percentage of cell nuclei that the neural networks indicated as positive. The percentage of positive cells from healthy persons was in the range 8% to 38%. The percentage of positive cells collected near the lesions was in the range 31% to 96%. The percentages from the healthy side of the oral cavity of patients with lesions ranged 37% to 89%. For each fold, it was possible to find a threshold for the number of positive cells that would correctly classify all patients as normal or positive, even for the samples taken from the healthy side of the oral cavity. The network based on the ResNet architecture showed slightly better performance than the VGG-based one. Conclusion: Our small pilot study indicates that malignancyassociated changes that can be detected by neural networks may exist among cells in the oral cavity of patients with precancerous lesions. We are currently collecting samples from more patients, and will present those results as well, with our poster at CYTO 2018.

  • 9.
    Bengtsson, Ewert
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Robust cell image segmentation methods2004In: Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, ISSN 1054-6618, Vol. 14, no 2, p. 157-167Article in journal (Refereed)
    Abstract [en]

    Biomedical cell image analysis is one of the main application fields of computerized image analysis. This paper outlines the field and the different analysis steps related to it. Relative advantages of different approaches to the crucial step of image segmentation are discussed. Cell image segmentation can be seen as a modeling problem where different approaches are more or less explicitly based on cell models. For example, thresholding methods can be seen as being based on a model stating that cells have an intensity that is different from the surroundings. More robust segmentation can be obtained if a combination of features, such as intensity, edge gradients, and cellular shape, is used. The seeded watershed transform is proposed as the most useful tool for incorporating such features into the cell model. These concepts are illustrated by three real-world problems.

  • 10. Bogren, Karin
    et al.
    Gamstedt, Kristofer
    Berthold, Fredrik
    Lindström, Mikael
    Nygård, Per
    Malmberg, Filip
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Axelsson, Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Svensson, Stina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Borgefors, Gunilla
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Stress transfer and failure in pulpfibre reinforced composites: Effects of microstructure characterized by Xray microtomography2006In: 2006 Progress in Paper Physics: A seminar, 2006Conference paper (Other academic)
  • 11.
    Bombrun, Maxime
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Allalou, Amin
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Partel, Gabriele
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Solorzano, Leslie
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Qian, Xiaoyan
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Tomtebodavagen 23, S-17165 Solna, Sweden.
    Nilsson, Mats
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Tomtebodavagen 23, S-17165 Solna, Sweden.
    Wählby, Carolina
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Decoding gene expression in 2D and 3D2017In: Image Analysis: Part II, Springer, 2017, p. 257-268Conference paper (Refereed)
    Abstract [en]

    Image-based sequencing of RNA molecules directly in tissue samples provides a unique way of relating spatially varying gene expression to tissue morphology. Despite the fact that tissue samples are typically cut in micrometer thin sections, modern molecular detection methods result in signals so densely packed that optical “slicing” by imaging at multiple focal planes becomes necessary to image all signals. Chromatic aberration, signal crosstalk and low signal to noise ratio further complicates the analysis of multiple sequences in parallel. Here a previous 2D analysis approach for image-based gene decoding was used to show how signal count as well as signal precision is increased when analyzing the data in 3D instead. We corrected the extracted signal measurements for signal crosstalk, and improved the results of both 2D and 3D analysis. We applied our methodologies on a tissue sample imaged in six fluorescent channels during five cycles and seven focal planes, resulting in 210 images. Our methods are able to detect more than 5000 signals representing 140 different expressed genes analyzed and decoded in parallel.

  • 12.
    Cristea, Alexander
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience.
    Karlsson Edlund, Patrick
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Qaisar, Rizwan
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Larsson, Lars
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Clinical Neurophysiology.
    Effects of ageing and gender on the spatial organisation of nuclei in single human skeletal muscle cells2009In: Neuromuscular Disorders, ISSN 0960-8966, E-ISSN 1873-2364, Vol. 19, no 8, p. 605-606Article in journal (Refereed)
  • 13.
    Cristea, Alexander
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Clinical Neurophysiology.
    Karlsson Edlund, Patrick
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Qaisar, Rizwan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Clinical Neurophysiology.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Larsson, Lars
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Clinical Neurophysiology.
    Effects of ageing and gender on the spatial organization of nuclei in single human skeletal muscle cells2009In: Neuromuscular Disorders, ISSN 0960-8966, E-ISSN 1873-2364, Vol. 19, p. 605-606Article in journal (Refereed)
  • 14.
    Curic, Vladimir
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Faculty of Technical Sciences, University of Novi Sad, Serbia.
    Distance measures between digital fuzzy objects and their applicability in image processing2011In: Combinatorial Image Analysis / [ed] Jake Aggarwal, Reneta Barneva, Valentin Brimkov, Kostadin Koroutchev, Elka Koroutcheva, Springer Berlin/Heidelberg, 2011, p. 385-397Conference paper (Refereed)
    Abstract [en]

    We present two different extensions of the Sum of minimal distances and the Complement weighted sum of minimal distances to distances between fuzzy sets. We evaluate to what extent the proposed distances show monotonic behavior with respect to increasing translation and rotation of digital objects, in noise free, as well as in noisy conditions. Tests show that one of the extension approaches leads to distances exhibiting very good performance. Furthermore, we evaluate distance based classification of crisp and fuzzy representations of objects at a range of resolutions. We conclude that the proposed distances are able to utilize the additional information available in a fuzzy representation, thereby leading to improved performance of related image processing tasks.

  • 15.
    Curic, Vladimir
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Faculty of Engineering, University of Novi Sad, Serbia.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. University of Novi Sad, Serbia.
    Sarve, Hamid
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Borgefors, Gunilla
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    A new set distance and its application to shape registration2014In: Pattern Analysis and Applications, ISSN 1433-7541, E-ISSN 1433-755X, Vol. 17, no 1, p. 141-152Article in journal (Refereed)
  • 16.
    Delic, Marija
    et al.
    University of Novi Sad, Faculty of technical sciences.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia.
    αLBP – a novel member of the Local Binary Pattern family based on α-cutting2015In: Proc. 9th International Symposium on Image and Signal Processing and Analysis, IEEE , 2015, p. 13-18Conference paper (Refereed)
    Abstract [en]

    Local binary pattern (LBP) descriptors have been popular in texture classification in recent years. They were introduced as descriptors of local image texture and their histograms are shown to be well performing texture features. In this paper we introduce two new LBP descriptors, αLBP and its improved variant IαLBP. We evaluate their performance in classification by comparing them with some of the existing LBP descriptors - LBP, ILBP, shift LBP (SLBP) and with one ternary descriptor - LTP. The texture descriptors are evaluated on three datasets - KTH-TIPS2b, UIUC and Virus texture dataset. The novel descriptor outperforms the other descriptors on two datasets, KTH-TIPS2b and Virus, and is tied for first place with ILBP on the UIUC dataset.

  • 17. Drazic, Slobodan
    et al.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Estimation of Feret's diameter from pixel coverage representation of a shape2016In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 80, p. 37-45Article in journal (Refereed)
  • 18.
    Dražić, Slobodan
    et al.
    Faculty of Technical Sciences, University of Novi Sad, Serbia.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Faculty of Technical Sciences, University of Novi Sad, Serbia.
    Precise Estimation of the Projection of a Shape from a Pixel Coverage Representation2011In: Proceedings of the 7th IEEE International Symposium on Image and Signal Processing and Analysis (ISPA), IEEE Computer Society, 2011, p. 569-574Conference paper (Refereed)
    Abstract [en]

    Measuring width and diameter of a shape areproblems well studied in the literature. A pixel coverage repre-sentation is one specific type of digital fuzzy representation of acontinuous image object, where the (membership) value of eachpixel is (approximately) equal to the relative area of the pixelwhich is covered by the continuous object. Lately a number ofmethods for shape analysis use pixel coverage for reducing errorof estimation. We introduce a novel method for estimating theprojection of a shape in a given direction. The method is based onutilizing pixel coverage representation of a shape. Performance ofthe method is evaluated by a number of tests on synthetic objects,confirming high precision and applicability for calculation ofdiameter and elongation of a shape.

  • 19.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Azar, Jimmy
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Busch, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology.
    Carlbom, Ingrid
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Blind Color Decomposition of Histological Images2013In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 32, no 6, p. 983-994Article in journal (Refereed)
    Abstract [en]

    Cancer diagnosis is based on visual examination under a microscope of tissue sections from biopsies. But whereas pathologists rely on tissue stains to identify morphological features, automated tissue recognition using color is fraught with problems that stem from image intensity variations due to variations in tissue preparation, variations in spectral signatures of the stained tissue, spectral overlap and spatial aliasing in acquisition, and noise at image acquisition. We present a blind method for color decomposition of histological images. The method decouples intensity from color information and bases the decomposition only on the tissue absorption characteristics of each stain. By modeling the charge-coupled device sensor noise, we improve the method accuracy. We extend current linear decomposition methods to include stained tissues where one spectral signature cannot be separated from all combinations of the other tissues' spectral signatures. We demonstrate both qualitatively and quantitatively that our method results in more accurate decompositions than methods based on non-negative matrix factorization and independent component analysis. The result is one density map for each stained tissue type that classifies portions of pixels into the correct stained tissue allowing accurate identification of morphological features that may be linked to cancer.

  • 20.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Algorithms for cross-talk suppression in fluorescence microscopy2008In: Medicinteknikdagarna 2008, 2008, p. 64-64Conference paper (Other academic)
    Abstract [en]

     

     

     

  • 21.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Spectral Angle Histogram: a Novel Image Analysis Tool for Quantification of Colocalization and Cross-talk2009In: 9th International ELMI Meeting on Advanced Light Microscopy / [ed] Kurt Anderson, Gail McConnell, Glasgow, UK, 2009, p. 66-67Conference paper (Other academic)
    Abstract [en]

    In fluorescence microscopy, when analyzing spectral components, it is common to record two (or more) greyscale images. Each greyscale image, referred to as a channel, corresponds to intensities in different wavelength intervals. If each pixel of a two-channel image is plotted in a space spanned by the two intensity channels a conventional scatter-plot is obtained. Single-coloured pixels are distributed along the axes, while colocalized pixels are distributed closer to the diagonal of the scatter-plot, and cross-talk (as well as noise) is observed as deviations of the single-coloured vectors from the axes. Detection of colocalized pixels is often based on a division of this 2D space into different regions by intensity thresholding. We have developed a method for reducing the scatter-plot to a 1D spectral angle histogram through a series of steps that compensate for the quantization noise which is always present in digital image data.

    Using the spectral angle histogram, we can quantify colocalization in a fully automated and robust manner. As compared to previous methods for quantification of colocalization, this approach is insensitive to cross-talk. In fact, it can also be employed to quantify and compensate for cross-talk, using either linear unmixing or fuzzy classification by spectral angle, ensuring complete suppression of cross-talk with minimal loss of information. Recently we started investigating how the method can deal with autofluorescence. Initial tests on real image data show that the method may be useful for improved background suppression and amplification of the true signals.

    The article “Quantification of colocalization and cross-talk based on spectral angles”, describing the method, is about to be published in the Journal of Microscopy. Authors have also filed a patent application “Pixel classification in image analysis” in 2008.

  • 22.
    Gupta, Anindya
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Suveer, Amit
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Bajic, Buda
    Faculty of Technical Sciences, University of Novi Sad, Serbia.
    Pepic, Ivana
    Faculty of Technical Sciences, University of Novi Sad, Serbia.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Denoising of Short Exposure Transmission Electron Microscopy Images using CNN2018In: Swedish Symposium on Image Analysis, 2018Conference paper (Other academic)
  • 23. Gupta, Anindya
    et al.
    Suveer, Amit
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Dragomir, Anca
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Clinical and experimental pathology.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    False positive reduction of cilia detected in low resolution TEM images using a convolutional neural network2017In: Swedish Symposium on Image Analysis, Swedish Society for Automated Image Analysis , 2017Conference paper (Other academic)
  • 24.
    Gupta, Anindya
    et al.
    Tallinn Univ Technol, TJ Seebeck Dept Elect, Tallinn, Estonia.
    Suveer, Amit
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Serbian Acad Arts & Sci, Math Inst, Belgrade, Serbia.
    Dragomir, Anca
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Clinical and experimental pathology.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Vironova AB, Stockholm, Sweden.
    Sladoje, Nataša
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Serbian Acad Arts & Sci, Math Inst, Belgrade, Serbia.
    Convolutional neural networks for false positive reduction of automatically detected cilia in low magnification TEM images2017In: Image Analysis: Part I, Springer, 2017, p. 407-418Conference paper (Refereed)
    Abstract [en]

    Automated detection of cilia in low magnification transmission electron microscopy images is a central task in the quest to relieve the pathologists in the manual, time consuming and subjective diagnostic procedure. However, automation of the process, specifically in low magnification, is challenging due to the similar characteristics of non-cilia candidates. In this paper, a convolutional neural network classifier is proposed to further reduce the false positives detected by a previously presented template matching method. Adding the proposed convolutional neural network increases the area under Precision-Recall curve from 0.42 to 0.71, and significantly reduces the number of false positive objects.

  • 25.
    Höglund, Anna-Stina
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Liu, Jingxia
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Karlsson, Patrick
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Borgefors, Gunilla
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Larsson, Lars
    The spatial distribution of nuclei in single skeletal muscle cells as visualised by 3-D images:: the differences in organisation between species and between healthy cells and cells affected by disease2007In: Biophysical Journal: 637A-637A Suppl. S, 2007Conference paper (Other scientific)
  • 26.
    Ilic, Vladimir
    et al.
    Faculty of Engineering, University of Novi Sad, Serbia.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Faculty of Engineering, University of Novi Sad, Serbia.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Faculty of Engineering, University of Novi Sad, Serbia.
    Precise Euclidean distance transforms in 3D from voxel coverage representation2015In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 65, p. 184-191Article in journal (Refereed)
    Abstract [en]

    Distance transforms (DTs) are, usually, defined on a binary image as a mapping from each background element to the distance between its centre and the centre of the closest object element. However, due to discretization effects, such DTs have limited precision, including reduced rotational and translational invariance. We show in this paper that a significant improvement in performance of Euclidean DTs can be achieved if voxel coverage values are utilized and the position of an object boundary is estimated with sub-voxel precision. We propose two algorithms of linear time complexity for estimating Euclidean DT with sub-voxel precision. The evaluation confirms that both algorithms provide 4-14 times increased accuracy compared to what is achievable from a binary object representation.

  • 27.
    Ilic, Vladimir
    et al.
    Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbi.
    Sladoje, Nataša
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbi.
    Signature of a Shape Based on Its Pixel Coverage Representation2016In: Discrete Geometry for Computer Imagery, DGCI 2016. Lecture Notes in Computer Science, Vol. 9647, pp. 181-193, Springer 2016: 19th IAPR International Conference, DGCI 2016, Nantes, France, April 18-20, 2016. Proceedings / [ed] Normand, Nicolas, Guédon, Jeanpierre, Autrusseau, Florent, Springer Berlin/Heidelberg, 2016, Vol. 9647, p. 181-193Conference paper (Refereed)
    Abstract [en]

    Distance from the boundary of a shape to its centroid, a.k.a. signature of a shape, is a frequently used shape descriptor. Commonly, the observed shape results from a crisp (binary) segmentation of an image. The loss of information associated with binarization leads to a significant decrease in accuracy and precision of the signature, as well as its reduced invariance w.r.t. translation and rotation. Coverage information enables better estimation of edge position within a pixel. In this paper, we propose an iterative method for computing the signature of a shape utilizing its pixel coverage representation. The proposed method iteratively improves the accuracy of the computed signature, starting from a good initial estimate. A statistical study indicates considerable improvements in both accuracy and precision, compared to a crisp approach and a previously proposed approach based on averaging signatures over α-cuts of a fuzzy representation. We observe improved performance of the proposed descriptor in the presence of noise and reduced variation under translation and rotation.

  • 28.
    Karlsson Edlund, Patrick
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Non-uniform 3D distance transform for anisotropic signal correction in confocal image volumes of skeletal muscle cell nuclei2008In: Proc. 5th International Symposium on Biomedical Imaging, Piscataway, NJ: IEEE , 2008, p. 1363-1366Conference paper (Refereed)
  • 29.
    Karlsson, Patrick
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Correction in 3D Confocal Images2008In: Proceedings of the 2008 Symposium on Image Analysis, 2008, p. 31-34Conference paper (Other academic)
  • 30. Karlsson, Patrick
    et al.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Segmentation and separation of point like fluorescent markers in digital images2004In: ISBI2004, 2004, p. 1291-1294Conference paper (Refereed)
  • 31.
    Karlsson, Patrick
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Höglund, Anna-Stina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience. Klinisk neurofysiologi.
    Liu, Jingxia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience. Klinisk neurofysiologi.
    Larsson, Lars
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience. Klinisk neurofysiologi.
    Analysis of Skeletal Fibers in Three Dimensional Images2007In: Medicinteknikdagarna 2007, 2007, p. 1-Conference paper (Other (popular science, discussion, etc.))
  • 32.
    Karlsson, Patrick
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Höglund, Anna-Stina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience. Clinical Neurophysiology.
    Liu, Jingxia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience. Clinical Neurophysiology.
    Larsson, Lars
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience. Clinical Neurophysiology.
    Analysis of Skeletal Fibers in Three Dimensional Images: Methodological considerations2007In: XXXVIth European Muscle Conference of the European Society for Muscle Research: European Muscle Conference 2007, 2007, p. 130-Conference paper (Other academic)
    Abstract [en]

    Knowledge of the detailed three dimensional organization of nuclei in skeletal muscle fibers is of fundamental importance for the understanding of the basic mechanisms involved in muscle wasting associated with for example neuromuscular disorders and aging. An ongoing interdisciplinary collaboration between the Centre for Image Analysis (CBA), and the Muscle Research Group (MRG), both at Uppsala University, addresses the issue of spatial distribution of myonuclei using confocal microscopic techniques together with advanced methods for computerized image analysis.

    Performing quantitative analysis on true three dimensional volume images captured by confocal microscopy gives us the option to perform in-depth statistical analysis of the relationship between neighboring myonuclei. The three dimensional representation enables extraction of a number of features for individual myonuclei, e.g., size and shape of a nucleus, and the myonuclear domain (in which each myonucleus control the gene products). This project investigates data sets from single muscle fibers sampled from mouse, rat, pig, human, horse and rhino to determine the myonuclei arrangement between species with a 100,000 fold difference in body weight.

    The appropriate image analysis tools needed for gaining the understanding of organization in three dimensional volume images are developed within the project to facilitate the analysis of similarities between species, and unique features within a species. The accumulated understanding of the spatial organization of myonuclei, and the effect of individual myonuclei size, will lead to an increased knowledge of basic mechanisms underlying muscle wasting in various neuromuscular disorders. This knowledge will hopefully lead to new therapeutic strategies that can be evaluated in experimental animal models prior to clinical testing trials in patients.

  • 33.
    Karlsson, Patrick
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Segmentation of point-like fluorescent markers2004In: Proceedings: Symposium on Image Analysis, 2004, p. 146-149Conference paper (Other academic)
    Abstract [en]

    We present a method for accurate segmentation of point like signals, from fluorescent markers in digital microscopic images with subcellular resolution. The method is able to segment and separate clustered signals, which facilitates accurate dot counting. The method performance is evaluated using synthetic images, that are modeled after real digital microscopy images of cells. The results show that the method is able to detect point like fluorescent signals as correct as a manual operator.

  • 34.
    Lidayová, Kristína
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Sladoje, Nataša
    Frimmel, Hans
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
    Coverage segmentation of thin structures by linear unmixing and local centre of gravity attraction2013In: Proc. 8th International Symposium on Image and Signal Processing and Analysis, IEEE Signal Processing Society, 2013, p. 83-88Conference paper (Refereed)
  • 35.
    Lidayová, Kristína
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Sladoje, Nataša
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Frimmel, Hans
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
    Wang, Chunliang
    Smedby, Örjan
    Coverage segmentation of 3D thin structures2015In: Proc. 5th International Conference on Image Processing Theory, Tools and Applications, Piscataway, NJ: IEEE , 2015, p. 23-28Conference paper (Refereed)
  • 36.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Development of Algorithms for Digital Image Cytometry2002Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis presents work in digital image cytometry applied to fluorescence microscope images of cultivated cells. Focus has been on the development and compilation of robust image analysis tools, enabling quantitative measurements of various properties of cells and cell structures. A significant part of the work has consisted of developing robust segmentation methods for fluorescently labelled cells. This, in combination with effort applied in the areas of feature extraction and statistical data analysis, has enabled the compilation of a complete chain of processing steps to produce a system capable of performing fully automatic segmentation and classification of fluorescently labelled cells according to their level of activation.

    Two sequences of processing steps, both leading to automatic cytoplasm segmentation of fluorescence microscopy cell images are presented. In one of the sequences, an additional image of the nuclei of the cells is segmented. The nuclei are then used as seeds for the segmentation of the cytoplasm image. This solves the problem of over-segmentation of the cytoplasms in an efficient way. The other sequence uses merge and split algorithms on the cytoplasm image, in conjunction with statistical analysis of descriptive features. This analysis is used in a feedback system to improve the segmentation performance, and to give an overall quality measure of the segmentation.

    A classification method that separates individual cells into three classes, depending on their level of activation, is described. The method is based on analysis of time series of images. Using both general purpose features and carefully designed problem specific features, in combination with a floating feature selection procedure, a Bayesian classifier is built. Evaluation showed that the performance of the fully automatic classification procedure was very close to the performance of skilled manual classification.

    A novel method for performing estimation of intensity nonuniformites of microscope images is presented. Methods to solve many other problems related to image analysis of cell images are discussed and evaluated. All methods presented in this work are applicable to real-world situations. The two main projects of the thesis work have been performed in close cooperation with and according to demands of the biomedical industry.

    List of papers
    1. Image Analysis for Automatic Segmentation of Cytoplasms and Classification of Rac1 Activation
    Open this publication in new window or tab >>Image Analysis for Automatic Segmentation of Cytoplasms and Classification of Rac1 Activation
    2004 (English)In: Cytometry, ISSN 0196-4763, E-ISSN 1097-0320, Vol. 57A, no 1, p. 22-23Article in journal (Refereed) Published
    Abstract [en]

    BACKGROUND:

    Rac1 is a GTP-binding molecule involved in a wide range of cellular processes. Using digital image analysis, agonist-induced translocation of green fluorescent protein (GFP) Rac1 to the cellular membrane can be estimated quantitatively for individual cells.

    METHODS:

    A fully automatic image analysis method for cell segmentation, feature extraction, and classification of cells according to their activation, i.e., GFP-Rac1 translocation and ruffle formation at stimuli, is described. Based on training data produced by visual annotation of four image series, a statistical classifier was created.

    RESULTS:

    The results of the automatic classification were compared with results from visual inspection of the same time sequences. The automatic classification differed from the visual classification at about the same level as visual classifications performed by two different skilled professionals differed from each other. Classification of a second image set, consisting of seven image series with different concentrations of agonist, showed that the classifier could detect an increased proportion of activated cells at increased agonist concentration.

    CONCLUSIONS:

    Intracellular activities, such as ruffle formation, can be quantified by fully automatic image analysis, with an accuracy comparable to that achieved by visual inspection. This analysis can be done at a speed of hundreds of cells per second and without the subjectivity introduced by manual judgments.

    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:uu:diva-90085 (URN)10.1002/cyto.a.10107 (DOI)14699602 (PubMedID)
    Available from: 2002-12-19 Created: 2002-12-19 Last updated: 2018-12-02Bibliographically approved
    2. Algorithms for cytoplasm segmentation of fluorescence labeled cells
    Open this publication in new window or tab >>Algorithms for cytoplasm segmentation of fluorescence labeled cells
    Show others...
    2002 (English)In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 24, no 2-3, p. 101-111Article in journal (Refereed) Published
    Abstract [en]

    Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre-processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of correctly segmented single cells can be further processed by a splitting step. By statistical analysis we therefore get a feedback system for separation of clustered cells. After the segmentation is completed, the quality of the final segmentation is evaluated. By training the algorithm on a representative set of training images, the algorithm is made fully automatic for subsequent images created under similar conditions. Automatic cytoplasm segmentation was tested on CHO-cells stained with calcein. The fully automatic method showed between 89% and 97% correct segmentation as compared to manual segmentation.

    National Category
    Biological Sciences
    Identifiers
    urn:nbn:se:uu:diva-90086 (URN)12446959 (PubMedID)
    Available from: 2002-12-19 Created: 2002-12-19 Last updated: 2018-12-18
    3. Statistical quality control for segmentation of fluorescence labeled cells
    Open this publication in new window or tab >>Statistical quality control for segmentation of fluorescence labeled cells
    Show others...
    2001 (English)In: In Proceedings of the 5th Korea-Germany Joint Workshop on Advanced Medical Image Processing, Seoul, KoreaArticle in journal (Refereed) Published
    Abstract
    Identifiers
    urn:nbn:se:uu:diva-90087 (URN)
    Available from: 2002-12-19 Created: 2002-12-19 Last updated: 2018-12-02Bibliographically approved
    4. A comparison of methods for estimation of intensity nonuniformities in 2D and 3D microscope images of fluorescence stained cells
    Open this publication in new window or tab >>A comparison of methods for estimation of intensity nonuniformities in 2D and 3D microscope images of fluorescence stained cells
    2001 (English)In: In Proceedings of the 12th Scandinavian Conference on Image Analysis (SCIA), Bergen, Norway, p. 264-271Article in journal (Refereed) Published
    Abstract
    Identifiers
    urn:nbn:se:uu:diva-90088 (URN)
    Available from: 2002-12-19 Created: 2002-12-19 Last updated: 2018-12-02Bibliographically approved
    5. Histogram thresholding using kernel density estimates
    Open this publication in new window or tab >>Histogram thresholding using kernel density estimates
    2000 (English)In: In Proceedings of the Swedish Society for Automated Image Analysis (SSAB) Symposium on Image Analysis, Halmstad, Sweden, p. 41-44Article in journal (Refereed) Published
    Abstract
    Identifiers
    urn:nbn:se:uu:diva-90089 (URN)
    Available from: 2002-12-19 Created: 2002-12-19 Last updated: 2018-12-02Bibliographically approved
  • 37.
    Lindblad, Joakim
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Surface Area Estimation of Digitized 3D Objects using Weighted Local Configurations2005In: Image and Vision Computing, Vol. 23, no 2, p. 111-122Article in journal (Refereed)
    Abstract [en]

    We present a method for estimating surface area of three-dimensional objects in discrete binary images. A surface area weight is assigned to each 2×2×2 configuration of voxels. The total surface area of a digital object is given by a summation of the local area contributions. Optimal area weights are derived in order to provide an unbiased estimate with minimum variance for randomly oriented digitized planar surfaces. Due to co-appearance of certain voxel combinations, the optimal solution is not uniquely defined for planar surfaces. A Monte Carlo-based optimization of the estimator performance on the distribution of digitized balls of increasing radii is performed in order to uniquely determine the optimal surface area weights. The method is further evaluated on various objects in a range of sizes. A significant reduction of the error for small objects is observed. The algorithm is appealingly simple; the use of only a small local neighborhood enables efficient implementations in hardware and/or in parallel architectures.

  • 38.
    Lindblad, Joakim
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Surface Volume Estimation of Digitized Hyperplanes Using Weighted Local Configurations2005In: Proceedings of Discrete Geometry for Computer Imagery, 2005, p. 252-262Conference paper (Refereed)
    Abstract [en]

    We present a method for estimating the surface volume of four-dimensional objects in discrete binary images. A surface volume weight is assigned to each 2 × 2 × 2 × 2 configuration of image elements. The total surface volume of a digital 4D object is given by a summation of the local volume contributions. Optimal volume weights are derived in order to provide an unbiased estimate with minimal variance for randomly oriented digitized planar hypersurfaces. Only 14 out of 64 possible boundary configurations appear on planar hypersurfaces. We use a marching hypercubes tetrahedrization to assign surface volume weights to the non-planar cases. The correctness of the method is verified on four-dimensional balls and cubes digitized in different sizes. The algorithm is appealingly simple; the use of only a local neighbourhood enables efficient implementations in hardware and/or in parallel architectures.

  • 39. Lindblad, Joakim
    et al.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sladoje, Nataša
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Microscopy image enhancement for cost-effective cervical cancer screening2015In: Image Analysis, Springer, 2015, p. 440-451Conference paper (Refereed)
    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.

  • 40.
    Lindblad, Joakim
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia.
    Curic, Vladimir
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Faculty of Technical Sciences, University of Novi Sad.
    On set distances and their application to image registration2009In: Proc. 6th International Symposium on Image and Signal Processing and Analysis, Salzburg, Austria: IEEE , 2009, p. 449-454Conference paper (Refereed)
    Abstract [en]

    In this paper we study set distances that are used in image processing. We propose a generalization of Sum of minimal distances and show that its special cases include a metric by Symmetric difference. The Hausdorff metric and the Chamfer matching distances are also closely related with the presented framework. In addition, we define the Complement set distance of a given distance. We evaluate the observed distance with respect to applicability to image object registration. We perform comparative evaluations with respect to noise sensitivity, as well as with respect to rigid body transformations. We conclude that the family of Generalized sum of minimal distances has many desirable properties for this application.

  • 41.
    Lindblad, Joakim
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lukic, Tibor
    Sladoje, Natasa
    Defuzzification by Feature Distance Minimization Based on DC Programming2007In: 5th International Symposium on Image and Signal Processing and Analysis, 2007: ISPA 2007, 2007, p. 373-378Conference paper (Refereed)
    Abstract [en]

    We introduce the use of DC programming, in combination with convex-concave regularization, as a deterministic approach for solving the optimization problem imposed by defuzzification by feature distance minimization. We provide a DC based algorithm for finding a solution to the defuzzification problem by expressing the objective function as a difference of two convex functions and iteratively solving a family of DC programs. We compare the performance with the previously recommended method, simulated annealing, on a number of test images. Encouraging results, together with several advantages of the DC based method, approve use of this approach, and motivate its further exploration.

  • 42.
    Lindblad, Joakim
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Exact linear time Euclidean distance transforms of grid line sampled shapes2015In: Mathematical Morphology and its Applications to Signal and Image Processing, Springer, 2015, p. 645-656Conference paper (Refereed)
    Abstract [en]

    We propose a method for computing, in linear time, the exact Euclidean distance transform of sets of points s. t. one coordinate of a point can be assigned any real value, whereas other coordinates are restricted to discrete sets of values. The proposed distance transform is applicable to objects represented by grid line sampling, and readily provides sub-pixel precise distance values. The algorithm is easy to implement; we present complete pseudo code. The method is easy to parallelize and extend to higher dimensional data. We present two ways of obtaining approximate grid line sampled representations, and evaluate the proposed EDT on synthetic examples. The method is competitive w. r. t. state-of-the-art methods for sub-pixel precise distance evaluation.

  • 43.
    Lindblad, Joakim
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Faculty of Technical Sciences, University of Novi Sad.
    Lukic, Tibor
    University of Novi Sad, Faculty of Technical Sciences.
    De-noising of SRµCT Fiber Images by Total Variation Minimization2010In: Proceedings of the 20th International Conference on Pattern Recognition (ICPR10), Istanbul, Turkey, 2010, p. 4621-4624Conference paper (Refereed)
    Abstract [en]

    SRμCT images of paper and pulp fiber materials are characterized by a low signal to noise ratio. De-noising is therefore a common preprocessing step before segmentation into fiber and background components. We suggest a de-noising method based on total variation minimization using a modified Spectral Conjugate Gradient algorithm. Quantitative evaluation performed on synthetic 3D data and qualitative evaluation on real 3D paper fiber data confirm appropriateness of the suggested method for the particular application.

  • 44.
    Lindblad, Joakim
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Lukic, Tibor
    Feature Based Defuzzification in Z² and Z³ Using a Scale Space Approach2006In: Discrete Geometry for Computer Imagery 13th International Conference, DGCI 2006, Szeged, Hungary, October 25-27, 2006. Proceedings: DGCI 2006, 2006, p. 379-390Conference paper (Refereed)
    Abstract [en]

    A defuzzification method based on feature distance minimization is further improved by incorporating into the distance function feature values measured on object representations at different scales. It is noticed that such an approach can improve defuzzification results by better preserving the properties of a fuzzy set; area preservation at scales in-between local (pixel-size) and global (the whole object) provides that characteristics of the fuzzy object are more appropriately exhibited in the defuzzification. For the purpose of comparing sets of different resolution, we propose a feature vector representation of a (fuzzy and crisp) set, utilizing a resolution pyramid. The distance measure is accordingly adjusted. The defuzzification method is extended to the 3D case. Illustrative examples are given.

  • 45. Lindblad, Joakim
    et al.
    Sladoje, Natasa
    Malm, Patrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Moshavegh, Ramin
    Mehnert, Andrew
    Optimizing optics and imaging for pattern recognition based screening tasks2014In: Proc. 22nd International Conference on Pattern Recognition, IEEE Computer Society, 2014, p. 3333-3338Conference paper (Refereed)
    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.

  • 46.
    Lindblad, Joakim
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Suveer, Amit
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Dragomir, Anca
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    High-resolution reconstruction by feature distance minimization from multiple views of an object2015In: Proc. 5th International Conference on Image Processing Theory, Tools and Applications, Piscataway, NJ: IEEE , 2015, p. 29-34Conference paper (Refereed)
    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.

  • 47.
    Lindblad, Joakim
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Faculty of Technical Sciences, University of Novi Sad.
    Linear time distances between fuzzy sets with applications to pattern matching and classification2014In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 23, no 1, p. 126-136Article in journal (Refereed)
    Abstract [en]

    We present four novel point-to-set distances defined for fuzzy or gray-level image data, two based on integration over α-cuts and two based on the fuzzy distance transform. We explore their theoretical properties. Inserting the proposed point-to-set distances in existing definitions of set-to-set distances, among which are the Hausdorff distance and the sum of minimal distances, we define a number of distances between fuzzy sets. These set distances are directly applicable for comparing gray-level images or fuzzy segmented objects, but also for detecting patterns and matching parts of images. The distance measures integrate shape and intensity/membership of observed entities, providing a highly applicable tool for image processing and analysis. Performance evaluation of derived set distances in real image processing tasks is conducted and presented. It is shown that the considered distances have a number of appealing theoretical properties and exhibit very good performance in template matching and object classification for fuzzy segmented images as well as when applied directly on gray-level intensity images. Examples include recognition of hand written digits and identification of virus particles. The proposed set distances perform excellently on the MNIST digit classification task, achieving the best reported error rate for classification using only rigid body transformations and a kNN classifier.

  • 48.
    Lukic, Tibor
    et al.
    University of Novi Sad, Faculty of technical sciences.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia.
    Regularized image denoising based on spectral gradient optimization2011In: Inverse Problems, ISSN 0266-5611, E-ISSN 1361-6420, Vol. 27, no 8, p. 085010:1-17Article in journal (Refereed)
    Abstract [en]

    Image restoration methods, such as denoising, deblurring, inpainting, etc, are often based on the minimization of an appropriately defined energy function. We consider energy functions for image denoising which combine a quadratic data-fidelity term and a regularization term, where the properties of the latter are determined by a used potential function. Many potential functions are suggested for different purposes in the literature. We compare the denoising performance achieved by ten different potential functions. Several methods for efficient minimization of regularized energy functions exist. Most are only applicable to particular choices of potential functions, however. To enable a comparison of all the observed potential functions, we propose to minimize the objective function using a spectral gradient approach; spectral gradient methods put very weak restrictions on the used potential function. We present and evaluate the performance of one spectral conjugate gradient and one cyclic spectral gradient algorithm, and conclude from experiments that both are well suited for the task. We compare the performance with three total variation-based state-of-the-art methods for image denoising. From the empirical evaluation, we conclude that denoising using the Huber potential (for images degraded by higher levels of noise; signal-to-noise ratio below 10 dB) and the Geman and McClure potential (for less noisy images), in combination with the spectral conjugate gradient minimization algorithm, shows the overall best performance.

  • 49.
    Malmberg, Filip
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis.
    Sladoje, Natasa
    Faculty of Technical Sciences, University of Novi Sad.
    Nyström, Ingela
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    A Graph-based Framework for Sub-pixel Image Segmentation2011In: Theoretical Computer Science, ISSN 0304-3975, E-ISSN 1879-2294, Vol. 412, no 15, p. 1338-1349Article in journal (Refereed)
    Abstract [en]

    Many image segmentation methods utilize graph structures for representing images, where the flexibility and generality of the abstract structure is beneficial. By using a fuzzy object representation, i.e., allowing partial belongingness of elements to image objects, the unavoidable loss of information when representing continuous structures by finite sets is significantly reduced,enabling feature estimates with sub-pixel precision.This work presents a framework for object representation based on fuzzysegmented graphs. Interpreting the edges as one-dimensional paths betweenthe vertices of a graph, we extend the notion of a graph cut to that of a located cut, i.e., a cut with sub-edge precision. We describe a method for computing a located cut from a fuzzy segmentation of graph vertices. Further,the notion of vertex coverage segmentation is proposed as a graph theoretic equivalent to pixel coverage segmentations and a method for computing such a segmentation from a located cut is given. Utilizing the proposed framework,we demonstrate improved precision of area measurements of synthetic two-dimensional objects. We emphasize that although the experiments presented here are performed on two-dimensional images, the proposed framework is defined for general graphs and thus applicable to images of any dimension.

  • 50.
    Malmberg, Filip
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Östlund, Catherine
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Almgren, Karin
    Gamstedt, E. Kristofer
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Applied Mechanics.
    Measurement of fibre–fibre contact in three-dimensional images of fibrous materials obtained from X-ray synchrotron microtomography2011In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, Vol. 637, no 1, p. 143-148Article in journal (Refereed)
12 1 - 50 of 84
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf