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Modified Gradient Search for Level Set Based Image Segmentation
Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
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.ORCID iD: 0000-0002-6457-4914
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.ORCID iD: 0000-0001-7557-4904
Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9267-2191
2013 (English)In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 22, no 2, 621-630 p.Article 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. Vol. 22, no 2, 621-630 p.
Keyword [en]
Active contours, gradient methods, image segmentation, level set method, machine learning, optimization, variational problems
National Category
Signal Processing Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-87658DOI: 10.1109/TIP.2012.2220148ISI: 000314717800017PubMedID: 23014748OAI: oai:DiVA.org:liu-87658DiVA: diva2:590026
Available from: 2013-01-21 Created: 2013-01-21 Last updated: 2017-12-06
In thesis
1. Level Set Segmentation and Volume Visualization of Vascular Trees
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. 86 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1543
Keyword
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: 2016-08-31Bibliographically approved

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Andersson, ThordLäthén, GunnarLenz, ReinerBorga, Magnus

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Center for Medical Image Science and Visualization, CMIVDepartment of Biomedical EngineeringThe Institute of TechnologyMedia and Information TechnologyMedical Informatics
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