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Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL. Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences. 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.
Calving events detection and quantification from time-lapse images in Tunabreen glacier2015In: Proc. 9th International Conference on Information & Communication Technology and Systems, Piscataway, NJ: IEEE , 2015, p. 61-65Conference paper (Refereed)
• 2.
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.
Flagship Biosci Inc, Westminster, CO USA. Lulea Univ Technol, Lulea, Sweden. 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 Approach to Mathematical Morphology on One Dimensional Sampled Signals2016In: IEEE Proceedings, International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, 2016, 2016Conference paper (Refereed)

We present a new approach to approximate continuous-domain mathematical morphology operators. The approach is applicable to irregularly sampled signals. We define a dilation under this new approach, where samples are duplicated and shifted according to the flat, continuous structuring element. We define the erosion by adjunction, and the opening and closing by composition. These new operators will significantly increase precision in image measurements. Experiments show that these operators indeed approximate continuous-domain operators better than the standard operators on sampled one-dimensional signals, and that they may be applied to signals using structuring elements smaller than the distance between samples. We also show that we can apply the operators to scan lines of a two-dimensional image to filter horizontal and vertical linear structures.

• 3.
University of Debrecen, Department of Computer Science, Debrecen Hungary .
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. Universit ́ de Nantes, IRCCyN UMR CNRS 6597, Nantes, France.
A Weight Sequence Distance Function2013In: : Mathematical Morphology and Its Applications to Signal and Image Processing / [ed] Cris L. Luengo Hendriks, Gunilla Borgefors, Robin Strand, Springer Berlin/Heidelberg, 2013, p. 292-301Conference paper (Refereed)

In this paper, a family of weighted neighborhood sequence distance functions defined on the square grid is presented. With this distance function, the allowed weight between any two adjacent pixels along a path is given by a weight sequence. We build on our previous results, where only two or three unique weights are considered, and present a framework that allows any number of weights. We show that the rotational dependency can be very low when as few as three or four unique weights are used. An algorithm for computing the distance transform (DT) that can be used for image processing applications is also presented.

• 4.
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.
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, Centre for Image Analysis. 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, Centre for Image Analysis.
Annual Report 20072008Report (Other (popular science, discussion, etc.))
• 5.
ISIT UMR6284 CNRS, Univ. d’Auvergne BP10448, F-63000 Clermont-Ferrand.
ISIT UMR6284 CNRS, Univ. d’Auvergne BP10448, F-63000 Clermont-Ferrand. 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. KEOSYS Company 1, impasse Auguste Fresnel, F 44815 Saint Herblain. ISIT UMR6284 CNRS, Univ. d’Auvergne BP10448, F-63000 Clermont-Ferrand.
Dual B-spline Snake for Interactive Myocardial Segmentation2013Conference paper (Refereed)

This paper presents a novel interactive segmentation formalism based on two coupledB-Spline snake models to efficiently and simultaneously extract myocardial walls fromshort-axis magnetic resonance images. The main added value of this model is interactionas it is possible to quickly and intuitively correct the result in complex cases withoutrestarting the whole segmentation working flow. During this process, energies computedfrom the images guide the user to the best position of the model.

• 6.
Uppsala University, Interfaculty Units, Centre for Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
Uppsala University, Interfaculty Units, Centre for Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
Centre for Image Analysis Annual Report 20032004Report (Other (popular scientific, debate etc.))
• 7.
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.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
An Approximation of the Maximal Inscribed Convex Set of a Digital Obj2005In: In F. Roli and S. Vitulano, editors, Proceedings of 13th International Conference on Image Analysis and Processing (ICIAP'05), 2005, p. 438-445Conference paper (Refereed)

In several application projects we have discovered the need of computing the maximal inscribed convex set of a digital shape. Here we present an algorithm for computing a reasonable approximation of this set, that can be used in both 2D and 3D. The main idea is to iteratively identify the deepest concavity and then remove it by cutting off as little as possible of the shape. We show results using both synthetic and real examples.

• 8. Brunner, David
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.
A High-Perpormance Parallel Thinning Approach Using a Non-Cubic Grid Structure.2006Report (Other academic)

In the past years the so-called body-centered cubic grid (bcc) has been examined and proved to be superior over Cartesian lattices for certain applications. Our work deals with parallel thinning on these bcc grids. We introduce conditions which are sufficient for retaining topology and suggest additional conditions to influence the shape of the resulting skeleton. We further developed an algorithm to extract curve skeletons out of 3d objects in parallel which we also present here.

We show in our results that the developed thinning approach on bcc grids is extremely efficient.

• 9. Ciesielski, Krzysztof Chris
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 Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology. 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 Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
Efficient algorithm for finding the exact minimum barrier distance2014In: Computer Vision and Image Understanding, ISSN 1077-3142, E-ISSN 1090-235X, Vol. 123, p. 53-64Article in journal (Refereed)
• 10.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Evolution and Developmental Biology.
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, 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, Biology, Department of Organismal Biology, Evolution and Developmental Biology.
Brain – Endocast relationship in the Australian lungfish, Neoceratodus forsteri, elucidated from tomographic data (Sarcopterygii: Dipnoi)2015In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 10, article id e0141277Article in journal (Refereed)

Although the brains of the three extant lungfish genera have been previously described, the spatial relationship between the brain and the neurocranium has never before been fully described nor quantified. Through the application of virtual microtomography (mu CT) and 3D rendering software, we describe aspects of the gross anatomy of the brain and labyrinth region in the Australian lungfish, Neoceratodus forsteri and compare this to previous accounts. Unexpected characters in this specimen include short olfactory peduncles connecting the olfactory bulbs to the telencephalon, and an oblong telencephalon. Furthermore, we illustrate the endocast (the mould of the internal space of the neurocranial cavity) of Neoceratodus, also describing and quantifying the brain-endocast relationship in a lungfish for the first time. Overall, the brain of the Australian lungfish closely matches the size and shape of the endocast cavity housing it, filling more than four fifths of the total volume. The forebrain and labyrinth regions of the brain correspond very well to the endocast morphology, while the midbrain and hindbrain do not fit so closely. Our results cast light on the gross neural and endocast anatomy in lungfishes, and are likely to have particular significance for palaeoneurologists studying fossil taxa.

• 11. Etterlin, P. E.
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.
Osteochondrosis, Synovial Fossae, and Articular Indentations in the Talus and Distal Tibia of Growing Domestic Pigs and Wild Boars2017In: Veterinary pathology, ISSN 0300-9858, E-ISSN 1544-2217, Vol. 54, no 3, p. 445-456Article in journal (Refereed)
• 12. Fouard, Céline
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.
Weighted Distance Expression in Modules2006In: Proceedings SSBA'06 Symposium on Image Analysis, 2006Conference paper (Other academic)

This paper presents the different properties of weighted distance and generalizes them to a global framework: modules. This allows to use weighted distance on unusal grids.

• 13. Fouard, Céline
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.
Weighted Distance Transforms Generalized To Modules and Their Computation on Point Lattices.2006Report (Other (popular science, discussion, etc.))
• 14.
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.
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. 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.
Weighted distance transforms generalized to modules and their computation on point lattices2007In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 40, no 9, p. 2453-2474Article in journal (Refereed)

This paper presents the generalization of weighted distances to modules and their computation through the chamfer algorithm on general point lattices. The first part is dedicated to formalization of definitions and properties (distance, metric, norm) of weighted distances on modules. It resumes tools found in literature to express the weighted distance of any point of a module and to compute optimal weights in the general case to get rotation invariant distances. The second part of this paper proves that, for any point lattice, the sequential two-scan chamfer algorithm produces correct distance maps. Finally, the definitions and computation of weighted distances are applied to the face-centered cubic (FCC) and body-centered cubic (BCC) grids.

• 15.
Department of Science and Technology, Linköping University.
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.
Anti-aliased Euclidean distance transform2011In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 32, no 2, p. 252-257Article in journal (Refereed)

We present a modified distance measure for use with distance transforms of anti-aliased, area sampled grayscale images of arbitrary binary contours. The modified measure can be used in any vector-propagation Euclidean distance transform. Our test implementation in the traditional SSED8 algorithm shows a considerable improvement in accuracy and homogeneity of the distance field compared to a traditional binary image transform. At the expense of a 10× slowdown for a particular image resolution, we achieve an accuracy comparable to a binary transform on a supersampled image with 16 × 16 higher resolution, which would require 256 times more computations and memory.

• 16.
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, 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. Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts MA, USA and Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. Dept. of Electronics and Communication Engineering (ECE), National Institute of Technology (NIT), Tiruchirappalli, India. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology. 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. 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. 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.
Automated classification of immunostaining patterns in breast tissue from the Human Protein Atlas2012In: Histopathology Image Analysis (HIMA): a MICCAI 2012 workshop, 2012Conference paper (Refereed)

Background:

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

Methods and Material:

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

Results:

Good results have been obtained by using the combinations of GLCM and wavelets and texture features, edge features, histograms, transforms, etc. (WND-CHARM). The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert.

Conclusions:

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

• 17.
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, 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. Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts MA, USA and Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. Dept. of Electronics and Communication Engineering (ECE), National Institute of Technology (NIT), Tiruchirappalli, India. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology, Molecular and Morphological Pathology. 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, 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. Uppsala University, Science for Life Laboratory, SciLifeLab. Broad Institute of Harvard and Massachusetts Institute Technology (MIT), Cambridge, Massachusetts, MA, USA, . 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.
Automated classification of immunostaining patterns in breast tissue from the Human Protein Atlas2013In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 4, no 14Article in journal (Refereed)

Background:

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

Materials and Methods:

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

Results:

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

Conclusions:

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

• 18.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Antaros Med, BioVenture Hub, Molndal, Sweden.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Antaros Med, BioVenture Hub, Molndal, Sweden. Sahlgrens Univ Hosp, Dept Radiol, Gothenburg, Sweden. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Antaros Med, BioVenture Hub, Molndal, Sweden. Univ Gothenburg, Sahlgrenska Acad, Inst Med, Gothenburg, Sweden. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Antaros Med, BioVenture Hub, Molndal, Sweden.
Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies.2017In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 10425Article in journal (Refereed)

Computed Tomography (CT) allows detailed studies of body composition and its association with metabolic and cardiovascular disease. The purpose of this work was to develop and validate automated and manual image processing techniques for detailed and efficient analysis of body composition from CT data. The study comprised 107 subjects examined in the Swedish CArdioPulmonary BioImage Study (SCAPIS) using a 3-slice CT protocol covering liver, abdomen, and thighs. Algorithms were developed for automated assessment of liver attenuation, visceral (VAT) and subcutaneous (SAT) abdominal adipose tissue, thigh muscles, subcutaneous, subfascial (SFAT) and intermuscular adipose tissue. These were validated using manual reference measurements. SFAT was studied in selected subjects were the fascia lata could be visually identified (approx. 5%). In addition, precision of manual measurements of intra- (IPAT) and retroperitoneal adipose tissue (RPAT) and deep- and superficial SAT was evaluated using repeated measurements. Automated measurements correlated strongly to manual reference measurements. The SFAT depot showed the weakest correlation (r = 0.744). Automated VAT and SAT measurements were slightly, but significantly overestimated (≤4.6%, p ≤ 0.001). Manual segmentation of abdominal sub-depots showed high repeatability (CV ≤ 8.1%, r ≥ 0.930). We conclude that the low dose CT-scanning and automated analysis makes the setup suitable for large-scale studies.

• 19.
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, 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.
Multimodal histological image registration using locally rigid transforms2015In: Proc. Interactive Medical Image Computing Workshop, 2015Conference paper (Refereed)

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

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

• 20.
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, 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.
Multimodal histological image registration using locally rigid transforms2014In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531Article in journal (Other academic)

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

• 21.
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, 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 histopathological tool for quantification of biomarkers with sub-cellular resolution2015In: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, ISSN 2168-1163, Vol. 3, no 1, p. 25-46Article in journal (Refereed)
• 22.
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, 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. Department of Electrical and Computer Engineering and the Department of Radiology, The University of Iowa, Iowa City, IA 52242 USA.
The Vectorial Minimum Barrier Distance2012In: International Conference on Pattern Recognition, ISSN 1051-4651, p. 792-795Article in journal (Refereed)

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

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

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

• 23. Linh, Troung Kieu
Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
Supercover of Non-square and Non-cubic Grids2004In: Proc. 10th International Workshop on Combinatorial Image Analysis (IWCIA 2004): Auckland, New Zealand, Dec. 2004, 2004, p. 88-97Conference paper (Refereed)

We define algebraic discrete geometry of hexagonal- and rhombic-dodecahedral- grids on a plane in a space, respectively. Since, a hexagon and a rhombic-dodecahedron are elements for tilling on a plane and in a space, respectively, a hexagon and a rhombic-dodecahedron are suitable as elements of discrete objects on a plane and in a space, respectively. For the description of linear objects in a discrete space, algebraic discrete geometry provides a unified treatment employing double Diophantus equations. In this paper, we introduce supercove for the hexagonal- and rhombic-dodecahedral- grid-systems on a plane and in a space, respectively.

• 24.
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, 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.
Comparison of Restoration Quality on Square and Hexagonal Grids using Normalized Convolution2012In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR), 2012Conference paper (Refereed)

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

• 25.
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, 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 Graph-Based Implementation of the Anti-Aliased Euclidean Distance Transform2014In: Proceedings 22nd International Conference on Pattern Recognition (ICPR), 2014, 2014, p. 1025-1030Conference paper (Refereed)

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

• 26.
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, 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.
Aliasing Properties of Voxels in Three-Dimensional Sampling Lattices2012In: Large Scale Scientific Computing, 2012, p. 507-514Conference paper (Refereed)
• 27.
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, 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.
Anti-Aliased Euclidean Distance Transform on 3D Sampling Lattices2014In: Discrete Geometry for Computer Imagery: 18th IAPR International Conference, DGCI 2014, Siena, Italy, September 10-12, 2014. Proceedings / [ed] Elena Barcucci, Andrea Frosini, Simone Rinaldi, 2014, p. 88-98Conference paper (Refereed)

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

• 28.
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, 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.
Comparison of restoration quality on square and hexagonal grids using normalized convolution2012In: Proc. 21st International Conference on Pattern Recognition, 2012, p. 3046-3049Conference paper (Refereed)
• 29. Litjens, Geert
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, 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.
Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge2014In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 18, no 2, p. 359-373Article in journal (Refereed)

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

• 30.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
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 Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Cell Biology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Antaros Medical, BioVenture Hub, Mölndal. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Antaros Medical, BioVenture Hub, Mölndal.
Automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images2017In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 3064Article in journal (Refereed)

Human brown adipose tissue (BAT), with a major site in the cervical-supraclavicular depot, is a promising anti-obesity target. This work presents an automated method for segmenting cervical-supraclavicular adipose tissue for enabling time-efficient and objective measurements in large cohort research studies of BAT. Fat fraction (FF) and R2* maps were reconstructed from water-fat magnetic resonance imaging (MRI) of 25 subjects. A multi-atlas approach, based on atlases from nine subjects, was chosen as automated segmentation strategy. A semi-automated reference method was used to validate the automated method in the remaining subjects. Automated segmentations were obtained from a pipeline of preprocessing, affine registration, elastic registration and postprocessing. The automated method was validated with respect to segmentation overlap (Dice similarity coefficient, Dice) and estimations of FF, R2* and segmented volume. Bias in measurement results was also evaluated. Segmentation overlaps of Dice = 0.93 +/- 0.03 (mean +/- standard deviation) and correlation coefficients of r > 0.99 (P < 0.0001) in FF, R2* and volume estimates, between the methods, were observed. Dice and BMI were positively correlated (r = 0.54, P = 0.03) but no other significant bias was obtained (P >= 0.07). The automated method compared well with the reference method and can therefore be suitable for time-efficient and objective measurements in large cohort research studies of BAT.

• 31.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
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 Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Cell Biology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
Magnetic resonance imaging cooling–reheating protocol indicates decreased fat fraction via lipid consumption in suspected brown adipose tissue2015In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 4, article id e0126705Article in journal (Refereed)

OBJECTIVES: To evaluate whether a water-fat magnetic resonance imaging (MRI) cooling-reheating protocol could be used to detect changes in lipid content and perfusion in the main human brown adipose tissue (BAT) depot after a three-hour long mild cold exposure.

MATERIALS AND METHODS: Nine volunteers were investigated with chemical-shift-encoded water-fat MRI at baseline, after a three-hour long cold exposure and after subsequent short reheating. Changes in fat fraction (FF) and R2*, related to ambient temperature, were quantified within cervical-supraclavicular adipose tissue (considered as suspected BAT, denoted sBAT) after semi-automatic segmentation. In addition, FF and R2* were quantified fully automatically in subcutaneous adipose tissue (not considered as suspected BAT, denoted SAT) for comparison. By assuming different time scales for the regulation of lipid turnover and perfusion in BAT, the changes were determined as resulting from either altered absolute fat content (lipid-related) or altered absolute water content (perfusion-related).

RESULTS: sBAT-FF decreased after cold exposure (mean change in percentage points = -1.94 pp, P = 0.021) whereas no change was observed in SAT-FF (mean = 0.23 pp, P = 0.314). sBAT-R2* tended to increase (mean = 0.65 s-1, P = 0.051) and SAT-R2* increased (mean = 0.40 s-1, P = 0.038) after cold exposure. sBAT-FF remained decreased after reheating (mean = -1.92 pp, P = 0.008, compared to baseline) whereas SAT-FF decreased (mean = -0.79 pp, P = 0.008, compared to after cold exposure).

CONCLUSIONS: The sustained low sBAT-FF after reheating suggests lipid consumption, rather than altered perfusion, as the main cause to the decreased sBAT-FF. The results obtained demonstrate the use of the cooling-reheating protocol for detecting changes in the cervical-supraclavicular fat depot, being the main human brown adipose tissue depot, in terms of lipid content and perfusion.

• 32.
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 Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology. 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 Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
SmartPaint: a tool for interactive segmentation of medical volume images2017In: COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION, ISSN 2168-1163, Vol. 5, no 1, p. 36-44Article in journal (Refereed)

We present SmartPaint, a general-purpose method and software for interactive segmentation of medical volume images. SmartPaint uses a novel paint-brush interaction paradigm, where the user segments objects in the image by 'sweeping' over them with the mouse cursor. The key feature of SmartPaint is that the painting tools adapt to the image content, selectively sticking to objects of interest while avoiding other structures. This behaviour is achieved by modulating the effect of the tools by both the Euclidean distance and the range distance (difference in image intensity values) from the mouse cursor. We evaluate SmartPaint on three publicly available medical image datasets, covering different image modalities and segmentation targets. The results show that, with a limited user effort, SmartPaint can produce segmentations whose accuracy is comparable to both the state-of-the-art automatic segmentation methods and manual delineations produced by expert users. The SmartPaint software is freely available, and can be downloaded from the authors' web page (http://www.cb.uu.se/similar to filip/SmartPaint/).

• 33.
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, 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 Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
Interactive Deformation of Volume Images for Image Registration2015In: Proc. Interactive Medical Image Computing Workshop, 2015Conference paper (Refereed)

Deformable image registration, the task of nding a spatial transformation that aligns two or more images with each other, is an important task in medical image analysis. To a large extent, research on image registration has been focused on automatic methods. This is in contrast to, e.g., image segmentation, where interactive semi-automatic methods are common. Here, we propose a method for interactive editing of a deformation eld aligning two volume images. The method has been implemented in a software that allows the user to click and drag points in the deformed image to a new location, while smoothly deforming surrounding points. The method is fast enough to allow real-time display of the deformed volume image during user interaction, on standard hardware. The resulting tool is useful for initializing automatic methods, and to correct errors in automatically generated registrations.

• 34.
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 Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
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 Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology. 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.
Smart Paint: A New Interactive Segmentation Method\\ Applied to MR Prostate Segmentation2012In: Prostate MR Image Segmentation Grand Challenge (PROMISE'12), a MICCAI 2012 workshop, 2012Conference paper (Refereed)

This paper describes a general method for interactive segmentation, Smart Paint. The user interaction is inspired by the way an airbrush is used, objects are segmented by "sweeping" with the mouse cursor in the image. The user adds or removes details in 3D by the proposed segmentation tool and the user interface shows the segmentation result in 2D slices through the object. We use the novel method for prostate segmentation in transversal T2-weighted MR images from multiple centers and vendors and with differences in scanning protocol.

The method was evaluated on the training set obtained from http://promise12.grand-challenge.org. In the first round, all 50 volumes were segmented and the mean of Dice's coefficient was 0.82 with standard deviation 0.09. In a second round, the first 30 volumes were re-segmented by the same user and the result was slightly improved -- Dice's coefficient 0.86 $\pm$ 0.05 was obtained. For the training data, the mean time to segment a volume was 3 minutes and 30 seconds.

The proposed method is a generic tool for interactive image segmentation and this paper illustrates that it is well-suited for prostate segmentation.

• 35.
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, 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 Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
An interactive tool for deformable registration of volume images2014In: Symposium of the Swedish Society for Automated Image Analysis (SSBA), 2014Conference paper (Other academic)
• 36.
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.
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. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
Seeded Segmentation Based on Object Homogeneity2012In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR), 2012, p. 21-24Conference paper (Refereed)

Seeded segmentation methods attempt to solve the segmentation problem in the presence of prior knowledge in the form of a partial segmentation, where a small subset of the image elements (seed-points) have been assigned correct segmentation labels. Common for most of the leading methods in this area is that they seek to find a segmentation where the boundaries of the segmented regions coincide with sharp edges in the image. Here, we instead propose a method for seeded segmentation that seeks to divide the image into areas of homogeneous pixel values. The method is based on the computation of minimal cost paths in a discrete representation of the image, using a novel path-cost function. The utility of the proposed method is demonstrated in a case study on segmentation of white matter hyperintensitities in MR images of the human brain.

• 37. Nagy, Benedek
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.
Approximating Euclidean circles by neighbourhood sequences in a hexagonal grid2011In: Theoretical Computer Science, ISSN 0304-3975, E-ISSN 1879-2294, Vol. 412, no 15, p. 1364-1377Article in journal (Refereed)
• 38. Nagy, Benedek
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.
Approximating Euclidean Distance Using Distances based on Neighbourhood Sequences in Non-Standard Three-Dimensional Grids.2006In: Proceedings Combinatorial Image Analysis: 11th International Workshop, IWCIA 2006, 2006, p. 89-100Conference paper (Refereed)

In image processing, it is often of great importance to have small rotational dependency for distance functions. We present an optimization for distances based on neighbourhood sequences for the face-centered cubic (fcc) and body-centered cubic (bcc) grids. In the optimization, several error functions are used measuring different geometrical properties of the balls obtained when using these distances.

• 39. Nagy, Benedek
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.
Neighborhood Sequences in the Diamond Grid2008In: Image analysis: from theory to applications, Research Publishing, Singapore, Chennai , 2008, p. 187-195Chapter in book (Other (popular science, discussion, etc.))

In digital image processing digital distances are useful; distances based on neighborhood sequences are widely used.

In this paper the diamond grid is considered, that is the three-dimensional grid of Carbon atoms in the diamond crystal. An algorithm to compute a shortest path defined by a neighborhood sequence between any two points in the diamond grid is presented. A formula to compute the distance based on neighborhood sequences is given. A necessary and sufficient condition to have a metric distance function is proved.

• 40.
Faculty of Informatics, University of Debrecen, Debrecen, Hungary.
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.
Neighborhood Sequences in the Diamond Grid - Algorithms with Four Neighbors2009In: Proceedings, theoretical track, International Workshop on Combinatorial Image Analysis (IWCIA 2009), Mexico, Berlin / Heidelberg: Springer , 2009, p. 109-121Conference paper (Refereed)

In digital image processing digital distances are useful; distances based on neighborhood sequences are widely used. In this paper the diamond grid is considered, that is the three-dimensional grid of Carbon atoms in the diamond crystal. This grid can be described by four coordinate values using axes of the directions of atomic bonds. In this way the sum of the coordinate values can be either zero or one. An algorithm to compute a shortest path defined by a neighborhood sequence between any two points in the diamond grid is presented. The metric and non-metric properties of some distances based on neighborhood sequences are also discussed. The constrained distance transformation and digital balls obtained by some distance functions are presented.

• 41.
Faculty of Informatics, University of Debrecen, Debrecen, Hungary.
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.
Neighborhood Sequences in the Diamond Grid: Algorithms with Two and Three Neighbors2009In: International journal of imaging systems and technology (Print), ISSN 0899-9457, E-ISSN 1098-1098, Vol. 19, no 2, p. 146-157Article in journal (Refereed)

In the digital image processing, digital distances are useful; distances based on neighborhood sequences are widely used. In this article, the diamond grid is considered, that is, the three-dimensional grid of carbon atoms in the diamond crystal. An algorithm to compute a shortest path defined by a neighborhood sequence between any two points in the diamond grid is presented. A formula to compute the distance based on neighborhood sequences with two neighborhood relations is given. The metric and nonmetric properties of some distances based on neighborhood sequences are also discussed. Finally, the constrained distance transformation is shown.

• 42.
Faculty of Informatics, University of Debrecen, Debrecen, Hungary.
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.
Neighborhood Sequences on nD Hexagonal/Face-Centered-Cubic Grids2009In: Proceedings, theoretical track, International Workshop on Combinatorial Image Analysis (IWCIA 2009), Mexico, Berlin / Heidelberg: Springer , 2009, p. 96-108Conference paper (Refereed)

The two-dimensional hexagonal grid and the three-dimensional face-centered cubic grid can be described by intersecting Z3 and Z4 with a (hyper)plane. Corresponding grids in higher dimensions (nD) are examined. In this paper, we define distance functions based on neighborhood sequences on these, higher dimensional generalizations of the hexagonal grid. An algorithm to produce a shortest path based on neighborhood sequences between any two gridpoints is presented. A formula to compute distance and condition of metricity are presented for neighborhood sequences using two types of neighbors. Distance transform as an application of these distances is also shown.

• 43.
LUNAM Université, Université de Nantes, IRCCyN UMR CNRS 6597, Polytech Nantes, Rue Christian Pauc, La Chantrerie, 44306 Nantes Cedex 3, France.
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.
Digital Distances and Integer Sequences2013In: Lecture Notes in Computer Science / [ed] Rocio Gonzalez-Diaz, Maria-Jose Jimenez, Belen Medran, 2013, p. 169-179Conference paper (Refereed)

In recent years, the theory behind distance functions defined by neighbourhood sequences has been developed in the digital geometry community. A neighbourhood sequence is a sequence of integers, where each element defines a neighbourhood. In this paper, we establish the equivalence between the representation of convex digital disks as an intersection of half-planes ( H -representation) and the expression of the distance as a maximum of non-decreasing functions.

Both forms can be deduced one from the other by taking advantage of the Lambek-Moser inverse of integer sequences.

Examples with finite sequences, cumulative sequences of periodic sequences and (almost) Beatty sequences are given. In each case, closed-form expressions are given for the distance function and H -representation of disks. The results can be used to compute the pair-wise distance between points in constant time and to find optimal parameters for neighbourhood sequences.

• 44. Normand, Nicolas
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.
Minimal-delay distance transform for neighborhood-sequence distances in 2D and 3D2013In: Computer Vision and Image Understanding, ISSN 1077-3142, E-ISSN 1090-235X, Vol. 117, no 4, p. 409-417Article in journal (Refereed)

This paper presents a path-based distance, where local displacement costs vary both according to the displacement vector and with the travelled distance. The corresponding distance transform algorithm is similar in its form to classical propagation-based algorithms, but the more variable distance increments are either stored in look-up-tables or computed on-the-fly. These distances and distance transform extend neighborhood-sequence distances, chamfer distances and generalized distances based on Minkowski sums. We introduce algorithms to compute a translated version of a neighborhood sequence distance map both for periodic and aperiodic sequences and a method to derive the centered distance map. A decomposition of the grid neighbors, in Z(2) and Z(3), allows to significantly decrease the number of displacement vectors needed for the distance transform. Overall, the distance transform can be computed with minimal delay, without the need to wait for the whole input image before beginning to provide the result image.

• 45.
IRCCyN UMR CNRS 6597, University of Nantes, France and School of Physics, Monash University, Melbourne, Australia .
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. IRCCyN UMR CNRS 6597, University of Nantes, France . IRCCyN UMR CNRS 6597, University of Nantes, France .
Path-Based Distance with Varying Weights andNeighborhood Sequences2011In: Proceedings, International Conference on Discrete Geometry for Computer Imagery (DGCI 2011): / [ed] Debled-Rennesson, Isabelle and Domenjoud, Eric and Kerautret, Bertrand and Even, Philippe, Berlin Heidelberg: Springer , 2011, p. 199-210Conference paper (Refereed)

This paper presents a path-based distance where local displacement costs vary both according to the displacement vector and with the travelled distance. The corresponding distance transform algorithm is similar in its form to classical propagation-based algorithms, but the more variable distance increments are either stored in look-up-tables or computed on-the-fly. These distances and distance transform extend neighborhood-sequence distances, chamfer distances and generalized distances based on Minkowski sums. We introduce algorithms to compute, in , a translated version of a neighborhood sequence distance map with a limited number of neighbors, both for periodic and aperiodic sequences. A method to recover the centered distance map from the translated one is also introduced. Overall, the distance transform can be computed with minimal delay, without the need to wait for the whole input image before beginning to provide the result image.

• 46. Normand, Nicolas
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 Streaming Distance Transform Algorithm for Neighborhood-Sequence Distances2014In: Image Processing On Line, ISSN 2105-1232, E-ISSN 2105-1232, Vol. 4, p. 196-203Article in journal (Refereed)

We describe an algorithm that computes a “translated” 2D Neighborhood-Sequence Distance Transform (DT) using a look up table approach. It requires a single raster scan of the input image and produces one line of output for every line of input. The neighborhood sequence is specified either by providing one period of some integer periodic sequence or by providing the rate of appearance of neighborhoods. The full algorithm optionally derives the regular (centered) DT from the “translated” DT, providing the result image on-the-ﬂy, with a minimal delay, before the input image is fully processed. Its efficiency can benefit all applications that use neighborhood- sequence distances, particularly when pipelined processing architectures are involved, or when the size of objects in the source image is limited.

• 47.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Cell Biology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Cancer and Vascular Biology. 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, 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.
Movement on Uneven Surfaces Displays Characteristic Features of Hop Diffusion2013In: Biophysical Journal, ISSN 0006-3495, E-ISSN 1542-0086, Vol. 104, no 2, p. 524A-524AArticle in journal (Other academic)
• 48. Saha, Punam K.
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, 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.
Digital topology and geometry in medical image processing: A survey2015In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 34, no 9, p. 1940-1964Article in journal (Refereed)

Digital topology and geometry refers to the use of topologic and geometric properties and features for images defined in digital grids. Such methods have been widely used in many medical imaging applications, including image segmentation, visualization, manipulation, interpolation, registration, surface-tracking, object representation, correction, quantitative morphometry etc. Digital topology and geometry play important roles in medical imaging research by enriching the scope of target outcomes and by adding strong theoretical foundations with enhanced stability, fidelity, and efficiency. This paper presents a comprehensive yet compact survey on results, principles, and insights of methods related to digital topology and geometry with strong emphasis on understanding their roles in various medical imaging applications. Specifically, this paper reviews methods related to distance analysis and path propagation, connectivity, surface-tracking, image segmentation, boundary and centerline detection, topology preservation and local topological properties, skeletonization, and object representation, correction, and quantitative morphometry. A common thread among the topics reviewed in this paper is that their theory and algorithms use the principle of digital path connectivity, path propagation, and neighborhood analysis.

• 49.
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 Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. 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 Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
Fuzzy Segmentation of Synthetic and MRI Volume Data sampled on Optimal Lattices2016Article in journal (Other academic)
• 50.
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, 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, 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.
LatticeLibrary and BccFccRaycaster: Software for processing and viewing 3D data on optimal sampling lattices2016In: SoftwareX, ISSN 2352-7110, Vol. 5, p. 16-24Article in journal (Refereed)
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