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  • 1.
    Andersson, Thord
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Läthén, Gunnar
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Lenz, Reiner
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    A Fast Optimization Method for Level Set Segmentation2009In: 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 (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.

  • 2.
    Andersson, Thord
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Läthén, Gunnar
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Lenz, Reiner
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Modified Gradient Search for Level Set Based Image Segmentation2013In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 22, no 2, p. 621-630Article in journal (Refereed)
    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.

  • 3.
    Johansson, Gunnar
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology, Digital Media.
    Nilsson, Ola
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    Söderström, Andreas
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Museth, Ken
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Distributed Ray Tracing in an Open Source Environment (Work in Progress)2006Conference paper (Refereed)
    Abstract [en]

    We present work in progress on concurrent ray tracing with distributed computers using ``off-the-shelf'' open source software. While there exists numerous open source ray tracers, very few offer support for state-of-the-art concurrent computing. However, it is a well known fact that ray tracing is computationally intensive and yet prevails as the preferred algorithm for photorealistic rendering. Thus, the current work is driven by a desire for a simple programming strategy (or recipe) that allows pre-existing ray tracing code to be parallelized on a heterogenous cluster of available office computers - strictly using open source components. Simplicity, stability, efficiency and modularity are the driving forces for this engineering project, and as such we do not claim any novel research contributions. However, we stress that this project grew out of a real-world need for a render cluster in our research group, and consequently our solutions have a significant practical value. In fact some of our results show a close to optimal speedup when considering the relative performances of each node. In this systems paper we aim at sharing these solutions and experiences with other members of the graphics community.

  • 4.
    Johansson Läthén, Gunnar
    et al.
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Museth, Ken
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Carr, Hamish
    School of Computer Science and Informatics, University College Dublin.
    Flexible and Topologically Localized Segmentation2007In: EuroVis07 Joint Eurographics: IEEE VGTC Symposium on Visualization / [ed] Ken Museth, Torsten Möller, and Anders Ynnerman, Aire-la-Ville, Switzerland: Eurographics Association , 2007, , p. 179-186p. 179-186Conference paper (Refereed)
    Abstract [en]

    One of the most common visualization tasks is the extraction of significant boundaries, often performed with iso- surfaces or level set segmentation. Isosurface extraction is simple and can be guided by geometric and topological analysis, yet frequently does not extract the desired boundary. Level set segmentation is better at boundary extrac- tion, but either leads to global segmentation without edges, [CV01], that scales unfavorably in 3D or requires an initial estimate of the boundary from which to locally solve segmentation with edges. We propose a hybrid system in which topological analysis is used for semi-automatic initialization of a level set segmentation, and geometric information bounded topologically is used to guide and accelerate an iterative segmentation algorithm that com- bines several state-of-the-art level set terms. We thus combine and improve both the flexible isosurface interface and level set segmentation without edges.

  • 5.
    Läthén, Gunnar
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Level Set Segmentation and Volume Visualization of Vascular Trees2013Doctoral 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.

    List of papers
    1. Flexible and Topologically Localized Segmentation
    Open this publication in new window or tab >>Flexible and Topologically Localized Segmentation
    2007 (English)In: EuroVis07 Joint Eurographics: IEEE VGTC Symposium on Visualization / [ed] Ken Museth, Torsten Möller, and Anders Ynnerman, Aire-la-Ville, Switzerland: Eurographics Association , 2007, , p. 179-186p. 179-186Conference paper, Published paper (Refereed)
    Abstract [en]

    One of the most common visualization tasks is the extraction of significant boundaries, often performed with iso- surfaces or level set segmentation. Isosurface extraction is simple and can be guided by geometric and topological analysis, yet frequently does not extract the desired boundary. Level set segmentation is better at boundary extrac- tion, but either leads to global segmentation without edges, [CV01], that scales unfavorably in 3D or requires an initial estimate of the boundary from which to locally solve segmentation with edges. We propose a hybrid system in which topological analysis is used for semi-automatic initialization of a level set segmentation, and geometric information bounded topologically is used to guide and accelerate an iterative segmentation algorithm that com- bines several state-of-the-art level set terms. We thus combine and improve both the flexible isosurface interface and level set segmentation without edges.

    Place, publisher, year, edition, pages
    Aire-la-Ville, Switzerland: Eurographics Association, 2007. p. 179-186
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-40841 (URN)54293 (Local ID)978-3-905673-45-6 (ISBN)54293 (Archive number)54293 (OAI)
    Conference
    Eurographics/ IEEE-VGTC Symposium on Visualization, 23-25 May, Norrköping, Sweden
    Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-09-19
    2. Blood vessel segmentation using multi-scale quadrature filtering
    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
    3. Non-ring Filters for Robust Detection of Linear Structures
    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
    4. Modified Gradient Search for Level Set Based Image Segmentation
    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
    5. Automatic Tuning of Spatially Varying Transfer Functions for Blood Vessel Visualization
    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
    6. Evaluation of transfer function methods in direct volume rendering of the blood vessel lumen
    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
  • 6.
    Läthén, Gunnar
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Segmentation Methods for Medical Image Analysis: Blood vessels, multi-scale filtering and level set methods2010Licentiate 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.

    List of papers
    1. Flexible and Topologically Localized Segmentation
    Open this publication in new window or tab >>Flexible and Topologically Localized Segmentation
    2007 (English)In: EuroVis07 Joint Eurographics: IEEE VGTC Symposium on Visualization / [ed] Ken Museth, Torsten Möller, and Anders Ynnerman, Aire-la-Ville, Switzerland: Eurographics Association , 2007, , p. 179-186p. 179-186Conference paper, Published paper (Refereed)
    Abstract [en]

    One of the most common visualization tasks is the extraction of significant boundaries, often performed with iso- surfaces or level set segmentation. Isosurface extraction is simple and can be guided by geometric and topological analysis, yet frequently does not extract the desired boundary. Level set segmentation is better at boundary extrac- tion, but either leads to global segmentation without edges, [CV01], that scales unfavorably in 3D or requires an initial estimate of the boundary from which to locally solve segmentation with edges. We propose a hybrid system in which topological analysis is used for semi-automatic initialization of a level set segmentation, and geometric information bounded topologically is used to guide and accelerate an iterative segmentation algorithm that com- bines several state-of-the-art level set terms. We thus combine and improve both the flexible isosurface interface and level set segmentation without edges.

    Place, publisher, year, edition, pages
    Aire-la-Ville, Switzerland: Eurographics Association, 2007. p. 179-186
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-40841 (URN)54293 (Local ID)978-3-905673-45-6 (ISBN)54293 (Archive number)54293 (OAI)
    Conference
    Eurographics/ IEEE-VGTC Symposium on Visualization, 23-25 May, Norrköping, Sweden
    Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-09-19
    2. Phase Based Level Set Segmentation of Blood Vessels
    Open this publication in new window or tab >>Phase Based Level Set Segmentation of Blood Vessels
    2008 (English)In: Proceedings of 19th International Conference on Pattern Recognition, IEEE Computer Society , 2008, p. 1-4Conference paper, Published paper (Refereed)
    Abstract [en]

    The segmentation and analysis of blood vessels hasreceived much attention in the research community. Theresults aid numerous applications for diagnosis andtreatment of vascular diseases. Here we use level setpropagation with local phase information to capture theboundaries of vessels. The basic notion is that localphase, extracted using quadrature filters, allows us todistinguish between lines and edges in an image. Notingthat vessels appear either as lines or edge pairs, weintegrate multiple scales and capture information aboutvessels of varying width. The outcome is a “global”phase which can be used to drive a contour robustly towardsthe vessel edges. We show promising results in2D and 3D. Comparison with a related method givessimilar or even better results and at a computationalcost several orders of magnitude less. Even with verysparse initializations, our method captures a large portionof the vessel tree.

    Place, publisher, year, edition, pages
    IEEE Computer Society, 2008
    Series
    International Conference on Pattern Recognition, ISSN 1051-4651
    National Category
    Medical Laboratory and Measurements Technologies
    Identifiers
    urn:nbn:se:liu:diva-21054 (URN)10.1109/ICPR.2008.4760970 (DOI)000264729000023 ()978-1-4244-2175-6 (ISBN)978-1-4244-2174-9 (ISBN)
    Conference
    19th International Conference on Pattern Recognition (ICPR 2008), 8-11 December 2008, Tampa, Finland
    Note

    ©2009 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, Jimmy Jonasson and Magnus Borga, Phase Based Level Set Segmentation of Blood Vessels, 2008, Proceedings of 19th International Conference on Pattern Recognition. http://dx.doi.org/10.1109/ICPR.2008.4760970

    Available from: 2009-09-28 Created: 2009-09-28 Last updated: 2015-10-09
    3. Momentum Based Optimization Methods for Level Set Segmentation
    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
    4. A Fast Optimization Method for Level Set Segmentation
    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
    5. Blood vessel segmentation using multi-scale quadrature filtering
    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
  • 7.
    Läthén, Gunnar
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Andersson, Thord
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Lenz, Reiner
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Level set based segmentation using gradient descent with momentum2009In: SSBA 2009, Symposium on image analysis,2009, Halmstad, Sweden: Halmstad University , 2009Conference paper (Other academic)
  • 8.
    Läthén, Gunnar
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Andersson, Thord
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Lenz, Reiner
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Momentum Based Optimization Methods for Level Set Segmentation2009In: 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 (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.

  • 9.
    Läthén, Gunnar
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Cros, Olivier
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Non-ring Filters for Robust Detection of Linear Structures2010In: Proceedings of the 20th International Conference on Pattern Recognition, Los Alamitos, CA, USA: IEEE Computer Society, 2010, p. 233-236Conference 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.

  • 10.
    Läthén, Gunnar
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Jonasson, Jimmy
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    Blood vessel segmentation using multi-scale quadrature filtering2010In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 31, no 8, p. 762-767Article in journal (Refereed)
    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.

  • 11.
    Läthén, Gunnar
    et al.
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Jonasson, Jimmy
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Phase Based Level Set Segmentation of Blood Vessels2008In: Proceedings of 19th International Conference on Pattern Recognition, IEEE Computer Society , 2008, p. 1-4Conference paper (Refereed)
    Abstract [en]

    The segmentation and analysis of blood vessels hasreceived much attention in the research community. Theresults aid numerous applications for diagnosis andtreatment of vascular diseases. Here we use level setpropagation with local phase information to capture theboundaries of vessels. The basic notion is that localphase, extracted using quadrature filters, allows us todistinguish between lines and edges in an image. Notingthat vessels appear either as lines or edge pairs, weintegrate multiple scales and capture information aboutvessels of varying width. The outcome is a “global”phase which can be used to drive a contour robustly towardsthe vessel edges. We show promising results in2D and 3D. Comparison with a related method givessimilar or even better results and at a computationalcost several orders of magnitude less. Even with verysparse initializations, our method captures a large portionof the vessel tree.

  • 12.
    Läthén, Gunnar
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Lindholm, Stefan
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Lenz, Reiner
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Evaluation of transfer function methods in direct volume rendering of the blood vessel lumen2014In: 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 (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

  • 13.
    Läthén, Gunnar
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Lindholm, Stefan
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    Lenz, Reiner
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Faculty of Health Sciences.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Automatic Tuning of Spatially Varying Transfer Functions for Blood Vessel Visualization2012In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 18, no 12, p. 2345-2354Article in journal (Refereed)
    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. 

  • 14.
    Läthén, Gunnar
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Museth, Ken
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Carr, Hamish
    School of Computer Science and Informatics University College Dublin.
    Topologically Localized Level Set Segmentation2007In: Symposium on Image Analysis,2007, 2007, p. 89-92Conference paper (Other academic)
1 - 14 of 14
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