Piecewise Constant Level Set Method for 3D Image Segmentation

  • Are Losnegård
  • Oddvar Christiansen
  • Xue-Cheng Tai
Conference paper

DOI: 10.1007/978-3-540-72823-8_59

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4485)
Cite this paper as:
Losnegård A., Christiansen O., Tai XC. (2007) Piecewise Constant Level Set Method for 3D Image Segmentation. In: Sgallari F., Murli A., Paragios N. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2007. Lecture Notes in Computer Science, vol 4485. Springer, Berlin, Heidelberg

Abstract

Level set methods have been proven to be efficient tools for tracing interface problems. Recently, some variants of the Osher- Sethian level set methods, which are called the Piecewise Constant Level Set Methods (PCLSM), have been proposed for some interface problems. The methods need to minimize a smooth cost functional under some special constraints. In this paper a PCLSM for 3D image segmentation is tested. The algorithm uses the gradient descent method combined with a Quasi-Newton method to minimize an augmented Lagrangian functional. Experiments for medical image segmentation are shown on synthetic three dimensional MR brain images. The efficiency of the algorithm and the quality of the obtained images are demonstrated.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Are Losnegård
    • 1
  • Oddvar Christiansen
    • 1
  • Xue-Cheng Tai
    • 1
  1. 1.Department of Mathematics, University of Bergen, Johannes Brunsgate 12, 5008 BergenNorway

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