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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
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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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