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Author:
Andersson, Thord (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))
Title:
A Fast Optimization Method for Level Set Segmentation
Department:
Linköping University, Center for Medical Image Science and Visualization (CMIV)
Linköping University, Department of Science and Technology, Digital Media
Linköping University, Department of Biomedical Engineering, Medical Informatics
Linköping University, The Institute of Technology
Publication type:
Conference paper (Refereed)
Language:
English
In:
Image Analysis: 16th Scandinavian Conference, SCIA 2009, Oslo, Norway, June 15-18, 2009. Proceedings
Editor:
A.-B. Salberg, J.Y. Hardeberg, and R. Jenssen
Conference:
16th Scandinavian Conference on Image Analysis, June 15-18 2009, Oslo, Norway
Publisher: Springer Berlin/Heidelberg
Series:
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online); 5575
Pages:
400-409
Year of publ.:
2009
URI:
urn:nbn:se:liu:diva-19313
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-19313
ISBN:
978-3-642-02229-6 (print), 978-3-642-02230-2 (online)
ISI:
000268661000041
Subject category:
Engineering and Technology
SVEP category:
TECHNOLOGY
Keywords(en) :
Image segmentation - level set method - optimization - gradient descent - Rprop - variational problems - active contours
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.

Available from:
2009-07-09
Created:
2009-06-17
Last updated:
2014-10-08
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