Target-oriented shape modeling with structure constraint for image segmentation

Wuxia Zhang, Yuan Yuan, Xuelong Li, Pingkun Yan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Image segmentation plays a critical role in medical imaging applications, whereas it is still a challenging problem due to the complex shapes and complicated texture of structures in medical images. Model based methods have been widely used for medical image segmentation as a priori knowledge can be incorporated. Accurate shape prior estimation is one of the major factors affecting the accuracy of model based segmentation methods. This paper proposes a novel statistical shape modeling method, which aims to estimate target-oriented shape prior by applying the constraint from the intrinsic structure of the training shape set. The proposed shape modeling method is incorporated into a deformable model based framework for image segmentation. The experimental results showed that the proposed method can achieve more accurate segmentation compared with other existing methods.

Original languageEnglish
Title of host publication1st Asian Conference on Pattern Recognition, ACPR 2011
Pages194-198
Number of pages5
DOIs
StatePublished - 2011
Externally publishedYes
Event1st Asian Conference on Pattern Recognition, ACPR 2011 - Beijing, China
Duration: 28 Nov 201128 Nov 2011

Publication series

Name1st Asian Conference on Pattern Recognition, ACPR 2011

Conference

Conference1st Asian Conference on Pattern Recognition, ACPR 2011
Country/TerritoryChina
CityBeijing
Period28/11/1128/11/11

Keywords

  • Image Segmentation
  • Manifold Assumption
  • Manifold Learning
  • Shape Modeling

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