A Fast Optimization Method for Level Set Segmentation

  • Thord Andersson
  • Gunnar Läthén
  • Reiner Lenz
  • Magnus Borga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)

Abstract

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.

Keywords

Image segmentation level set method optimization gradient descent Rprop variational problems active contours 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Thord Andersson
    • 1
    • 3
  • Gunnar Läthén
    • 2
    • 3
  • Reiner Lenz
    • 2
    • 3
  • Magnus Borga
    • 1
    • 3
  1. 1.Department of Biomedical EngineeringLinköping UniversitySweden
  2. 2.Department of Science and TechnologyLinköping UniversitySweden
  3. 3.Center for Medical Image Science and Visualization (CMIV)Linköping UniversitySweden

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