A new prior shape model for level set segmentation

Poay Hoon Lim*, Ulas Bagci, Li Bai

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Level set methods are effective for image segmentation problems. However, the methods suffer from limitations such as slow convergence and leaking problems. As such, over the past two decades, the original level set method has been evolved in many directions, including integration of prior shape models into the segmentation framework. In this paper, we introduce a new prior shape model for level set segmentation. With a shape model represented implicitly by a signed distance function, we incorporate a local shape parameter to the shape model. This parameter helps to regulate the model fitting process. Based on this local parameter of the shape model, we define a shape energy to drive the level set evolution for image segmentation. The shape energy is coupled with a Gaussian kernel, which acts as a weight distribution on the shape model. This Gaussian effect not only allows evolution of level set to deform around the shape model, but also provides a smoothing effect along the edges. Our approach presents a new dimension to extract local shape parameter directly from the shape model, which is different from previous work that focused on an indirect manner of feature extractions. Experimental results on synthetic, optical and MR images demonstrate the feasibility of this new shape model and shape energy.

Original languageEnglish (US)
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 16th Iberoamerican Congress, CIARP 2011, Proceedings
Number of pages8
StatePublished - 2011
Externally publishedYes
Event16th Iberoamerican Congress on Pattern Recognition, CIARP 2011 - Pucon, Chile
Duration: Nov 15 2011Nov 18 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7042 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th Iberoamerican Congress on Pattern Recognition, CIARP 2011


  • image segmentation
  • level set method
  • prior shape model
  • shape energy

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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