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Läthén, G., Lindholm, S., Lenz, R. & Borga, M. (2014). Evaluation of transfer function methods in direct volume rendering of the blood vessel lumen. In: Ivan Viola and Katja Buehler and Timo Ropinski (Ed.), Proceedings from the EG VCBM 2014. Eurographics Workshop on Visual Computing for Biology and Medicine, Vienna, Austria, September 4–5, 2014: . Paper presented at EG VCBM 2014. Eurographics Workshop on Visual Computing for Biology and Medicine, Vienna, Austria, September 4–5, 2014 (pp. 117-126). Eurographics - European Association for Computer Graphics
Open this publication in new window or tab >>Evaluation of transfer function methods in direct volume rendering of the blood vessel lumen
2014 (English)In: Proceedings from the EG VCBM 2014. Eurographics Workshop on Visual Computing for Biology and Medicine, Vienna, Austria, September 4–5, 2014 / [ed] Ivan Viola and Katja Buehler and Timo Ropinski, Eurographics - European Association for Computer Graphics, 2014, p. 117-126Conference paper, Published paper (Refereed)
Abstract [en]

Visualization of contrast enhanced blood vessels in CT angiography data presents a challenge due to varying concentration of the contrast agent. The purpose of this work is to evaluate the correctness (effectiveness) in visualizing the vessel lumen using two different 3D visualization strategies, thereby assessing the feasibility of using such visualizations for diagnostic decisions. We compare a standard visualization approach with a recent method which locally adapts to the contrast agent concentration. Both methods are evaluated in a parallel setting where the participant is instructed to produce a complete visualization of the vessel lumen, including both large and small vessels, in cases of calcified vessels in the legs. The resulting visualizations are thereafter compared in a slice viewer to assess the correctness of the visualized lumen. The results indicate that the participants generally overestimated the size of the vessel lumen using the standard visualization, whereas the locally adaptive method better conveyed the true anatomy. The participants did find the interpretation of the locally adaptive method to be less intuitive, but also noted that this did not introduce any prohibitive complexity in the work flow. The observed trends indicate that the visualized lumen strongly depends on the width and placement of the applied transfer function and that this dependency is inherently local rather than global. We conclude that methods that permit local adjustments, such as the method investigated in this study, can be beneficial to certain types of visualizations of large vascular trees

Place, publisher, year, edition, pages
Eurographics - European Association for Computer Graphics, 2014
Series
Eurographics Workshop on Visual Computing for Biology and Medicine, ISSN 2070-5778
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-97370 (URN)10.2312/vcbm.20141197 (DOI)978-3-905674-62-0 (ISBN)
Conference
EG VCBM 2014. Eurographics Workshop on Visual Computing for Biology and Medicine, Vienna, Austria, September 4–5, 2014
Available from: 2013-09-10 Created: 2013-09-10 Last updated: 2016-08-31Bibliographically approved
Läthén, G. (2013). Level Set Segmentation and Volume Visualization of Vascular Trees. (Doctoral dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Level Set Segmentation and Volume Visualization of Vascular Trees
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Medical imaging is an important part of the clinical workflow. With the increasing amount and complexity of image data comes the need for automatic (or semi-automatic) analysis methods which aid the physician in the exploration of the data. One specific imaging technique is angiography, in which the blood vessels are imaged using an injected contrast agent which increases the contrast between blood and surrounding tissue. In these images, the blood vessels can be viewed as tubular structures with varying diameters. Deviations from this structure are signs of disease, such as stenoses introducing reduced blood flow, or aneurysms with a risk of rupture. This thesis focuses on segmentation and visualization of blood vessels, consituting the vascular tree, in angiography images.

Segmentation is the problem of partitioning an image into separate regions. There is no general segmentation method which achieves good results for all possible applications. Instead, algorithms use prior knowledge and data models adapted to the problem at hand for good performance. We study blood vessel segmentation based on a two-step approach. First, we model the vessels as a collection of linear structures which are detected using multi-scale filtering techniques. Second, we develop machine-learning based level set segmentation methods to separate the vessels from the background, based on the output of the filtering.

In many applications the three-dimensional structure of the vascular tree has to be presented to a radiologist or a member of the medical staff. For this, a visualization technique such as direct volume rendering is often used. In the case of computed tomography angiography one has to take into account that the image depends on both the geometrical structure of the vascular tree and the varying concentration of the injected contrast agent. The visualization should have an easy to understand interpretation for the user, to make diagnostical interpretations reliable. The mapping from the image data to the visualization should therefore closely follow routines that are commonly used by the radiologist. We developed an automatic method which adapts the visualization locally to the contrast agent, revealing a larger portion of the vascular tree while minimizing the manual intervention required from the radiologist. The effectiveness of this method is evaluated in a user study involving radiologists as domain experts.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. p. 86
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1543
Keywords
level set methods, image segmentation, edge detection, visualization, volume rendering, blood vessels, angiography, vascular trees
National Category
Media and Communication Technology Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-97371 (URN)978-91-7519-514-8 (ISBN)
Public defence
2013-10-21, Domteatern, Visualiseringscenter C, Kungsgatan 54, Norrköping, 14:00 (English)
Opponent
Supervisors
Available from: 2013-09-11 Created: 2013-09-10 Last updated: 2018-01-11Bibliographically approved
Andersson, T., Läthén, G., Lenz, R. & Borga, M. (2013). Modified Gradient Search for Level Set Based Image Segmentation. IEEE Transactions on Image Processing, 22(2), 621-630
Open this publication in new window or tab >>Modified Gradient Search for Level Set Based Image Segmentation
2013 (English)In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 22, no 2, p. 621-630Article in journal (Refereed) Published
Abstract [en]

Level set methods are a popular way to solve the image segmentation problem. The solution contour is found by solving an optimization problem where a cost functional is minimized. Gradient descent methods are often used to solve this optimization problem since they are very easy to implement and applicable to general nonconvex functionals. They are, however, sensitive to local minima and often display slow convergence. Traditionally, cost functionals have been modified to avoid these problems. In this paper, we instead propose using two modified gradient descent methods, one using a momentum term and one based on resilient propagation. These methods are commonly used in the machine learning community. In a series of 2-D/3-D-experiments using real and synthetic data with ground truth, the modifications are shown to reduce the sensitivity for local optima and to increase the convergence rate. The parameter sensitivity is also investigated. The proposed methods are very simple modifications of the basic method, and are directly compatible with any type of level set implementation. Downloadable reference code with examples is available online.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2013
Keywords
Active contours, gradient methods, image segmentation, level set method, machine learning, optimization, variational problems
National Category
Signal Processing Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-87658 (URN)10.1109/TIP.2012.2220148 (DOI)000314717800017 ()23014748 (PubMedID)
Available from: 2013-01-21 Created: 2013-01-21 Last updated: 2017-12-06
Läthén, G., Lindholm, S., Lenz, R., Persson, A. & Borga, M. (2012). Automatic Tuning of Spatially Varying Transfer Functions for Blood Vessel Visualization. Paper presented at SciVis. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2345-2354
Open this publication in new window or tab >>Automatic Tuning of Spatially Varying Transfer Functions for Blood Vessel Visualization
Show others...
2012 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 18, no 12, p. 2345-2354Article in journal (Refereed) Published
Abstract [en]

Computed Tomography Angiography (CTA) is commonly used in clinical routine for diagnosing vascular diseases. The procedure involves the injection of a contrast agent into the blood stream to increase the contrast between the blood vessels and the surrounding tissue in the image data. CTA is often visualized with Direct Volume Rendering (DVR) where the enhanced image contrast is important for the construction of Transfer Functions (TFs). For increased efficiency, clinical routine heavily relies on preset TFs to simplify the creation of such visualizations for a physician. In practice, however, TF presets often do not yield optimal images due to variations in mixture concentration of contrast agent in the blood stream. In this paper we propose an automatic, optimization- based method that shifts TF presets to account for general deviations and local variations of the intensity of contrast enhanced blood vessels. Some of the advantages of this method are the following. It computationally automates large parts of a process that is currently performed manually. It performs the TF shift locally and can thus optimize larger portions of the image than is possible with manual interaction. The method is based on a well known vesselness descriptor in the definition of the optimization criterion. The performance of the method is illustrated by clinically relevant CT angiography datasets displaying both improved structural overviews of vessel trees and improved adaption to local variations of contrast concentration. 

Place, publisher, year, edition, pages
IEEE, 2012
National Category
Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-79365 (URN)10.1109/TVCG.2012.203 (DOI)000310143100038 ()
Conference
SciVis
Available from: 2012-07-15 Created: 2012-07-15 Last updated: 2017-12-07Bibliographically approved
Läthén, G., Jonasson, J. & Borga, M. (2010). Blood vessel segmentation using multi-scale quadrature filtering. Pattern Recognition Letters, 31(8), 762-767
Open this publication in new window or tab >>Blood vessel segmentation using multi-scale quadrature filtering
2010 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 31, no 8, p. 762-767Article in journal (Refereed) Published
Abstract [en]

The segmentation of blood vessels is a common problem in medical imagingand various applications are found in diagnostics, surgical planning, trainingand more. Among many dierent techniques, the use of multiple scales andline detectors is a popular approach. However, the typical line lters usedare sensitive to intensity variations and do not target the detection of vesselwalls explicitly. In this article, we combine both line and edge detection usingquadrature lters across multiple scales. The lter result gives well denedvessels as linear structures, while distinct edges facilitate a robust segmentation.We apply the lter output to energy optimization techniques for segmentationand show promising results in 2D and 3D to illustrate the behavior of ourmethod. The conference version of this article received the best paper award inthe bioinformatics and biomedical applications track at ICPR 2008.

Place, publisher, year, edition, pages
Elsevier, 2010
Keywords
Image segmentation, Blood vessels, Medical imaging, Multi-scale, Quadrature filter, Level set method
National Category
Medical Laboratory and Measurements Technologies
Identifiers
urn:nbn:se:liu:diva-21046 (URN)10.1016/j.patrec.2009.09.020 (DOI)000277552600014 ()
Note
Original Publication: Gunnar Läthén, Jimmy Jonasson and Magnus Borga, Blood vessel segmentation using multi-scale quadrature filtering, 2010, Pattern Recognition Letters, (31), 8, 762-767. http://dx.doi.org/10.1016/j.patrec.2009.09.020 Copyright: Elsevier Science B.V., Amsterdam. http://www.elsevier.com/ Available from: 2009-09-28 Created: 2009-09-28 Last updated: 2017-12-13
Läthén, G., Cros, O., Knutsson, H. & Borga, M. (2010). Non-ring Filters for Robust Detection of Linear Structures. In: Proceedings of the 20th International Conference on Pattern Recognition: . Paper presented at 20th International Conference on Pattern Recognition, Istanbul, Turkey, 23-26 August 2010 (pp. 233-236). Los Alamitos, CA, USA: IEEE Computer Society
Open this publication in new window or tab >>Non-ring Filters for Robust Detection of Linear Structures
2010 (English)In: Proceedings of the 20th International Conference on Pattern Recognition, Los Alamitos, CA, USA: IEEE Computer Society, 2010, p. 233-236Conference paper, Published paper (Refereed)
Abstract [en]

Many applications in image analysis include the problem of linear structure detection, e.g. segmentation of blood vessels in medical images, roads in satellite images, etc. A simple and efficient solution is to apply linear filters tuned to the structures of interest and extract line and edge positions from the filter output. However, if the filter is not carefully designed, artifacts such as ringing can distort the results and hinder a robust detection. In this paper, we study the ringing effects using a common Gabor filter for linear structure detection, and suggest a method for generating non-ring filters in 2D and 3D. The benefits of the non-ring design are motivated by results on both synthetic and natural images.

Place, publisher, year, edition, pages
Los Alamitos, CA, USA: IEEE Computer Society, 2010
Series
International Conference on Pattern Recognition, ISSN 1051-4651
Keywords
ringing filters, Gabor, non-ring filters, edge detection, filter design
National Category
Engineering and Technology Computer and Information Sciences Computer Vision and Robotics (Autonomous Systems) Signal Processing
Identifiers
urn:nbn:se:liu:diva-58850 (URN)10.1109/ICPR.2010.66 (DOI)978-1-4244-7542-1 (ISBN)
Conference
20th International Conference on Pattern Recognition, Istanbul, Turkey, 23-26 August 2010
Note

©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Gunnar Läthén, Olivier Cros, Hans Knutsson and Magnus Borga, Non-ring Filters for Robust Detection of Linear Structures, 2010, Proceedings of the 20th International Conference on Pattern Recognition, 233-236. http://dx.doi.org/10.1109/ICPR.2010.66

Available from: 2010-08-30 Created: 2010-08-30 Last updated: 2018-01-12Bibliographically approved
Läthén, G. (2010). Segmentation Methods for Medical Image Analysis: Blood vessels, multi-scale filtering and level set methods. (Licentiate dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Segmentation Methods for Medical Image Analysis: Blood vessels, multi-scale filtering and level set methods
2010 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Image segmentation is the problem of partitioning an image into meaningful parts, often consisting of an object and background. As an important part of many imaging applications, e.g. face recognition, tracking of moving cars and people etc, it is of general interest to design robust and fast segmentation algorithms. However, it is well accepted that there is no general method for solving all segmentation problems. Instead, the algorithms have to be highly adapted to the application in order to achieve good performance. In this thesis, we will study segmentation methods for blood vessels in medical images. The need for accurate segmentation tools in medical applications is driven by the increased capacity of the imaging devices. Common modalities such as CT and MRI generate images which simply cannot be examined manually, due to high resolutions and a large number of image slices. Furthermore, it is very difficult to visualize complex structures in three-dimensional image volumes without cutting away large portions of, perhaps important, data. Tools, such as segmentation, can aid the medical staff in browsing through such large images by highlighting objects of particular importance. In addition, segmentation in particular can output models of organs, tumors, and other structures for further analysis, quantification or simulation.

We have divided the segmentation of blood vessels into two parts. First, we model the vessels as a collection of lines and edges (linear structures) and use filtering techniques to detect such structures in an image. Second, the output from this filtering is used as input for segmentation tools. Our contributions mainly lie in the design of a multi-scale filtering and integration scheme for de- tecting vessels of varying widths and the modification of optimization schemes for finding better segmentations than traditional methods do. We validate our ideas on synthetical images mimicking typical blood vessel structures, and show proof-of-concept results on real medical images.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. p. 44
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1434
Keywords
Image segmentation, Medical image analysis, Level set method, Quadrature filter, Multi-scale
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-54181 (URN)LIU-TEK-LIC-2010:5 (Local ID)978-91-7393-410-7 (ISBN)LIU-TEK-LIC-2010:5 (Archive number)LIU-TEK-LIC-2010:5 (OAI)
Presentation
2010-04-15, K3, Kåkenhus, Campus Norrköping, Linköpings universitet, Norrköping, 13:00 (English)
Opponent
Supervisors
Available from: 2010-04-20 Created: 2010-03-01 Last updated: 2018-01-12Bibliographically approved
Andersson, T., Läthén, G., Lenz, R. & Borga, M. (2009). A Fast Optimization Method for Level Set Segmentation. In: A.-B. Salberg, J.Y. Hardeberg, and R. Jenssen (Ed.), Image Analysis: 16th Scandinavian Conference, SCIA 2009, Oslo, Norway, June 15-18, 2009. Proceedings. Paper presented at 16th Scandinavian Conference on Image Analysis, June 15-18 2009, Oslo, Norway (pp. 400-409). Springer Berlin/Heidelberg
Open this publication in new window or tab >>A Fast Optimization Method for Level Set Segmentation
2009 (English)In: Image Analysis: 16th Scandinavian Conference, SCIA 2009, Oslo, Norway, June 15-18, 2009. Proceedings / [ed] A.-B. Salberg, J.Y. Hardeberg, and R. Jenssen, Springer Berlin/Heidelberg, 2009, p. 400-409Conference paper, Published paper (Refereed)
Abstract [en]

Level set methods are a popular way to solve the image segmentation problem in computer image analysis. A contour is implicitly represented by the zero level of a signed distance function, and evolved according to a motion equation in order to minimize a cost function. This function defines the objective of the segmentation problem and also includes regularization constraints. Gradient descent search is the de facto method used to solve this optimization problem. Basic gradient descent methods, however, are sensitive for local optima and often display slow convergence. Traditionally, the cost functions have been modified to avoid these problems. In this work, we instead propose using a modified gradient descent search based on resilient propagation (Rprop), a method commonly used in the machine learning community. Our results show faster convergence and less sensitivity to local optima, compared to traditional gradient descent.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2009
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 5575
Keywords
Image segmentation - level set method - optimization - gradient descent - Rprop - variational problems - active contours
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-19313 (URN)10.1007/978-3-642-02230-2_41 (DOI)000268661000041 ()978-3-642-02229-6 (ISBN)978-3-642-02230-2 (ISBN)
Conference
16th Scandinavian Conference on Image Analysis, June 15-18 2009, Oslo, Norway
Available from: 2009-07-09 Created: 2009-06-17 Last updated: 2018-01-23Bibliographically approved
Läthén, G., Andersson, T., Lenz, R. & Borga, M. (2009). Level set based segmentation using gradient descent with momentum. In: SSBA 2009, Symposium on image analysis,2009. Halmstad, Sweden: Halmstad University
Open this publication in new window or tab >>Level set based segmentation using gradient descent with momentum
2009 (English)In: SSBA 2009, Symposium on image analysis,2009, Halmstad, Sweden: Halmstad University , 2009Conference paper, Published paper (Other academic)
Place, publisher, year, edition, pages
Halmstad, Sweden: Halmstad University, 2009
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-45370 (URN)82210 (Local ID)82210 (Archive number)82210 (OAI)
Projects
computer-graphics-image-processing/blood-vessel-segmentation
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2016-08-31
Läthén, G., Andersson, T., Lenz, R. & Borga, M. (2009). Momentum Based Optimization Methods for Level Set Segmentation. In: Xue-Cheng Tai, Knut Mørken, Marius Lysaker and Knut-Andreas Lie (Ed.), Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen (Ed.), Momentum Based Optimization Methods for Level Set Segmentation: Second International Conference, SSVM 2009, Voss, Norway, June 1-5, 2009. Proceedings. Paper presented at Second International Conference, SSVM 2009, June 1-5, Voss, Norway (pp. 124-136). Berlin: Springer Berlin/Heidelberg
Open this publication in new window or tab >>Momentum Based Optimization Methods for Level Set Segmentation
2009 (English)In: Momentum Based Optimization Methods for Level Set Segmentation: Second International Conference, SSVM 2009, Voss, Norway, June 1-5, 2009. Proceedings / [ed] Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen, Berlin: Springer Berlin/Heidelberg, 2009, p. 124-136Conference paper, Published paper (Refereed)
Abstract [en]

Segmentation of images is often posed as a variational problem. As such, it is solved by formulating an energy functional depending on a contour and other image derived terms. The solution of the segmentation problem is the contour which extremizes this functional. The standard way of solving this optimization problem is by gradient descent search in the solution space, which typically suffers from many unwanted local optima and poor convergence. Classically, these problems have been circumvented by modifying the energy functional. In contrast, the focus of this paper is on alternative methods for optimization. Inspired by ideas from the machine learning community, we propose segmentation based on gradient descent with momentum. Our results show that typical models hampered by local optima solutions can be further improved by this approach. We illustrate the performance improvements using the level set framework.

Place, publisher, year, edition, pages
Berlin: Springer Berlin/Heidelberg, 2009
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 5567
National Category
Medical Laboratory and Measurements Technologies
Identifiers
urn:nbn:se:liu:diva-21037 (URN)10.1007/978-3-642-02256-2_11 (DOI)000270543900011 ()3-642-02255-3 (ISBN)978-3-642-02255-5 (ISBN)978-3-642-02256-2 (ISBN)
Conference
Second International Conference, SSVM 2009, June 1-5, Voss, Norway
Note

Original Publication: Gunnar Läthén, Thord Andersson, Reiner Lenz and Magnus Borga, Momentum Based Optimization Methods for Level Set Segmentation, 2009, Lecture Notes in Computer Science 5567: Scale Space and Variational Methods in Computer Vision, 124-136. http://dx.doi.org/10.1007/978-3-642-02256-2_11 Copyright: Springer http://www.springerlink.com/

Available from: 2009-09-28 Created: 2009-09-28 Last updated: 2018-02-19Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6457-4914

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