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Nagy, B., Strand, R. & Normand, N. (2018). Distance Functions Based on Multiple Types of Weighted Steps Combined with Neighborhood Sequences. Journal of Mathematical Imaging and Vision, 60(8), 1209-1219
Open this publication in new window or tab >>Distance Functions Based on Multiple Types of Weighted Steps Combined with Neighborhood Sequences
2018 (English)In: Journal of Mathematical Imaging and Vision, ISSN 0924-9907, E-ISSN 1573-7683, Vol. 60, no 8, p. 1209-1219Article in journal (Refereed) Published
Abstract [en]

In this paper, we present a general framework for digital distance functions, defined as minimal cost paths, on the square grid. Each path is a sequence of pixels, where any two consecutive pixels are adjacent and associated with a weight. The allowed weights between any two adjacent pixels along a path are given by a weight sequence, which can hold an arbitrary number of weights. 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. Moreover, by using n weights, the Euclidean distance can be perfectly obtained on the perimeter of a square with side length 2n. A sufficient condition for weight sequences to provide metrics is proven.

Keywords
Distance functions, Weight sequences, Neighborhood sequences, Chamfer distances, Approximation of Euclidean distance
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-364167 (URN)10.1007/s10851-018-0805-1 (DOI)000443369800003 ()
Available from: 2018-11-05 Created: 2018-11-05 Last updated: 2018-11-16Bibliographically approved
Malmberg, F. & Strand, R. (2018). When Can lp-norm Objective Functions Be Minimized via Graph Cuts?. In: Barneva R., Brimkov V., Tavares J. (Ed.), Combinatorial Image Analysis: . Paper presented at International Workshop on Combinatorial Image Analysis (pp. 112-117). Springer
Open this publication in new window or tab >>When Can lp-norm Objective Functions Be Minimized via Graph Cuts?
2018 (English)In: Combinatorial Image Analysis / [ed] Barneva R., Brimkov V., Tavares J., Springer, 2018, p. 112-117Conference paper, Published paper (Refereed)
Abstract [en]

Techniques based on minimal graph cuts have become a standard tool for solving combinatorial optimization problems arising in image processing and computer vision applications. These techniques can be used to minimize objective functions written as the sum of a set of unary and pairwise terms, provided that the objective function is sub-modular. This can be interpreted as minimizing the l1-norm of the vector containing all pairwise and unary terms. By raising each term to a power p, the same technique can also be used to minimize the lp-norm of the vector. Unfortunately, the submodularity of an l1-norm objective function does not guarantee the submodularity of the corresponding lp-norm objective function. The contribution of this paper is to provide useful conditions under which an lp-norm objective function is submodular for all p>= 1, thereby identifying a large class of lp-norm objective functions that can be minimized via minimal graph cuts.

Techniques based on minimal graph cuts have become a standard tool for solving combinatorial optimization problems arising in image processing and computer vision applications. These techniques can be used to minimize objective functions written as the sum of a set of unary and pairwise terms, provided that the objective function is submodular. This can be interpreted as minimizing the l1l1-norm of the vector containing all pairwise and unary terms. By raising each term to a power p, the same technique can also be used to minimize the lplp-norm of the vector. Unfortunately, the submodularity of an l1l1-norm objective function does not guarantee the submodularity of the corresponding lplp-norm objective function. The contribution of this paper is to provide useful conditions under which an lplp-norm objective function is submodular for all p≥1p≥1, thereby identifying a large class of lplp-norm objective functions that can be minimized via minimal graph cuts.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Keywords
Minimal graph cuts, lp -norm, Submodularity
National Category
Discrete Mathematics Computer Sciences
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-366961 (URN)10.1007/978-3-030-05288-1_9 (DOI)978-3-030-05287-4 (ISBN)
Conference
International Workshop on Combinatorial Image Analysis
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2018-11-27
Strand, R., Malmberg, F., Johansson, L., Lind, L., Sundbom, M., Ahlström, H. & Kullberg, J. (2017). A concept for holistic whole body MRI data analysis, Imiomics. PLoS ONE, 12(2), Article ID e0169966.
Open this publication in new window or tab >>A concept for holistic whole body MRI data analysis, Imiomics
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2017 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 2, article id e0169966Article in journal (Refereed) Published
Abstract [en]

Purpose: To present and evaluate a whole-body image analysis concept, Imiomics (imaging omics) and an image registration method that enables Imiomics analyses by deforming all image data to a common coordinate system, so that the information in each voxel can be compared between persons or within a person over time and integrated with non-imaging data.

Methods: The presented image registration method utilizes relative elasticity constraints of different tissue obtained from whole-body water-fat MRI. The registration method is evaluated by inverse consistency and Dice coefficients and the Imiomics concept is evaluated by example analyses of importance for metabolic research using non-imaging parameters where we know what to expect. The example analyses include whole body imaging atlas creation, anomaly detection, and cross-sectional and longitudinal analysis.

Results: The image registration method evaluation on 128 subjects shows low inverse consistency errors and high Dice coefficients. Also, the statistical atlas with fat content intensity values shows low standard deviation values, indicating successful deformations to the common coordinate system. The example analyses show expected associations and correlations which agree with explicit measurements, and thereby illustrate the usefulness of the proposed Imiomics concept.

Conclusions: The registration method is well-suited for Imiomics analyses, which enable analyses of relationships to non-imaging data, e.g. clinical data, in new types of holistic targeted and untargeted big-data analysis.

National Category
Radiology, Nuclear Medicine and Medical Imaging
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-316830 (URN)10.1371/journal.pone.0169966 (DOI)000395934400002 ()28241015 (PubMedID)
Available from: 2017-02-27 Created: 2017-03-07 Last updated: 2017-11-29Bibliographically approved
Kullberg, J., Hedström, A., Brandberg, J., Strand, R., Johansson, L. E., Bergström, G. & Ahlström, H. (2017). Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies.. Scientific Reports, 7, Article ID 10425.
Open this publication in new window or tab >>Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies.
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2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 10425Article in journal (Refereed) Published
Abstract [en]

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.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-329273 (URN)10.1038/s41598-017-08925-8 (DOI)000409309300013 ()28874743 (PubMedID)
Funder
Swedish Research Council, 2012-2330Swedish Heart Lung FoundationVINNOVA
Available from: 2017-09-11 Created: 2017-09-11 Last updated: 2017-12-06Bibliographically approved
Lundström, E., Strand, R., Forslund, A., Bergsten, P., Weghuber, D., Ahlström, H. & Kullberg, J. (2017). Automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images. Scientific Reports, 7, Article ID 3064.
Open this publication in new window or tab >>Automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images
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2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 3064Article in journal (Refereed) Published
Abstract [en]

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.

National Category
Radiology, Nuclear Medicine and Medical Imaging
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-323968 (URN)10.1038/s41598-017-01586-7 (DOI)000402865000003 ()28596551 (PubMedID)
Funder
Swedish Research CouncilEU, FP7, Seventh Framework Programme, 279153
Available from: 2017-06-08 Created: 2017-06-12 Last updated: 2017-09-19Bibliographically approved
Malmberg, F., Luengo Hendriks, C. L. & Strand, R. (2017). Exact Evaluation of Targeted Stochastic Watershed Cuts. Discrete Applied Mathematics, 216(2), 449-460
Open this publication in new window or tab >>Exact Evaluation of Targeted Stochastic Watershed Cuts
2017 (English)In: Discrete Applied Mathematics, ISSN 0166-218X, E-ISSN 1872-6771, Vol. 216, no 2, p. 449-460Article in journal (Refereed) Published
Abstract [en]

Seeded segmentation with minimum spanning forests, also known as segmentation by watershed cuts, is a powerful method for supervised image segmentation. Given that correct segmentation labels are provided for a small set of image elements, called seeds, the watershed cut method completes the labeling for all image elements so that the boundaries between different labels are optimally aligned with salient edges in the image. Here, a randomized version of watershed segmentation, the targeted stochastic watershed, is proposed for performing multi-label targeted image segmentation with stochastic seed input. The input to the algorithm is a set of probability density functions (PDFs), one for each segmentation label, defined over the pixels of the image. For each pixel, we calculate the probability that the pixel is assigned a given segmentation label in seeded watershed segmentation with seeds drawn from the input PDFs. We propose an efficient algorithm (quasi-linear with respect to the number of image elements) for calculating the desired probabilities exactly.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Image segmentation, Stochastic watershed, Watershed cut, Minimum spanning forest
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-333808 (URN)10.1016/j.dam.2016.01.006 (DOI)000390504100011 ()
Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2018-09-04Bibliographically approved
Etterlin, P. E., Ekman, S., Strand, R., Olstad, K. & Ley, C. J. (2017). Osteochondrosis, Synovial Fossae, and Articular Indentations in the Talus and Distal Tibia of Growing Domestic Pigs and Wild Boars. Veterinary pathology, 54(3), 445-456
Open this publication in new window or tab >>Osteochondrosis, Synovial Fossae, and Articular Indentations in the Talus and Distal Tibia of Growing Domestic Pigs and Wild Boars
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2017 (English)In: Veterinary pathology, ISSN 0300-9858, E-ISSN 1544-2217, Vol. 54, no 3, p. 445-456Article in journal (Refereed) Published
National Category
Veterinary Science Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-322715 (URN)10.1177/0300985816688743 (DOI)000400089300012 ()28129094 (PubMedID)
Funder
Swedish Research Council Formas, 221-2013-317
Available from: 2017-01-27 Created: 2017-05-29 Last updated: 2017-06-01Bibliographically approved
Ahlström, H., Ekström, S., Sjöholm, T., Strand, R., Kullberg, J., Johansson, E., . . . Malmberg, F. (2017). Registration-based automated lesion detection and therapy evaluation of tumors in whole body PET-MR images. Paper presented at 42nd European-Society-for-Medical-Oncology Congress (ESMO), SEP 08-12, 2017, Madrid, SPAIN. Annals of Oncology, 28(S5), Article ID 78P.
Open this publication in new window or tab >>Registration-based automated lesion detection and therapy evaluation of tumors in whole body PET-MR images
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2017 (English)In: Annals of Oncology, ISSN 0923-7534, E-ISSN 1569-8041, Vol. 28, no S5, article id 78PArticle in journal, Meeting abstract (Other academic) Published
National Category
Radiology, Nuclear Medicine and Medical Imaging Cancer and Oncology
Identifiers
urn:nbn:se:uu:diva-346976 (URN)000411324000073 ()
Conference
42nd European-Society-for-Medical-Oncology Congress (ESMO), SEP 08-12, 2017, Madrid, SPAIN
Available from: 2018-03-26 Created: 2018-03-26 Last updated: 2018-03-26Bibliographically approved
Malmberg, F., Nordenskjöld, R., Strand, R. & Kullberg, J. (2017). SmartPaint: a tool for interactive segmentation of medical volume images. Computer Methods In Biomechanics And Biomedical Engeineering-Imaging And Visualization, 5(1), 36-44
Open this publication in new window or tab >>SmartPaint: a tool for interactive segmentation of medical volume images
2017 (English)In: Computer Methods In Biomechanics And Biomedical Engeineering-Imaging And Visualization, ISSN 2168-1163, Vol. 5, no 1, p. 36-44Article in journal (Refereed) Published
Abstract [en]

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/).

Place, publisher, year, edition, pages
TAYLOR & FRANCIS LTD, 2017
Keywords
image segmentation, medical imaging, interactive segmentation
National Category
Biomaterials Science
Identifiers
urn:nbn:se:uu:diva-319143 (URN)10.1080/21681163.2014.960535 (DOI)000396688800005 ()
Available from: 2017-03-31 Created: 2017-03-31 Last updated: 2018-05-14Bibliographically approved
Strand, R., Ciesielski, K. C., Malmberg, F. & Saha, P. K. (2017). The Minimum Barrier Distance: A Summary of Recent Advances. In: Lecture Notes in Computer Science book series (LNCS, volume 10502): . Paper presented at International Conference on Discrete Geometry for Computer Imagery (pp. 57-68). Switzerland, 10502
Open this publication in new window or tab >>The Minimum Barrier Distance: A Summary of Recent Advances
2017 (English)In: Lecture Notes in Computer Science book series (LNCS, volume 10502), Switzerland, 2017, Vol. 10502, p. 57-68Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present an overview and summary of recent results of the minimum barrier distance (MBD), a distance operator that is a promising tool in several image processing applications. The theory constitutes of the continuous MBD in Rn, its discrete formulation in Zn (in two different natural formulations), and of the discussion of convergence of discrete MBDs to their continuous counterpart. We describe two algorithms that compute MBD, one very fast but returning only approximate MBD, the other a bit slower, but returning the exact MBD. Finally, some image processing applications of MBD are presented and the directions of potential future research in this area are indicated.

Place, publisher, year, edition, pages
Switzerland: , 2017
National Category
Computer Sciences Discrete Mathematics
Identifiers
urn:nbn:se:uu:diva-333683 (URN)10.1007/978-3-319-66272-5_6 (DOI)978-3-319-66272-5 (ISBN)978-3-319-66271-8 (ISBN)
Conference
International Conference on Discrete Geometry for Computer Imagery
Available from: 2017-11-16 Created: 2017-11-16 Last updated: 2018-01-13Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7764-1787

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