Local region based medical image segmentation using J-divergence measures

Wanlin Zhu, Tianzi Jiang, Xiaobo Li

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

4 Scopus citations

Abstract

In this paper, we propose a novel variational formulation. The originality of our formulation is on the use of J-divergence (symmetrized Kullback-Leibler divergence) for the dissimilarity measure between local and global regions. The intensity of a local region is assumed to follow Gaussian distribution. Thus, two features - mean and variance of the distribution of every voxel are introduced to ensure the robustness of the algorithm when noise appeared. Then, J-divergence is used to measure the "distance" between two distributions. The proposed method is verified on synthetic and real medical images. The experimental results are very encouraging for medical image segmentation.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Pages7174-7177
Number of pages4
StatePublished - Dec 1 2005
Externally publishedYes
Event2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 - Shanghai, China
Duration: Sep 1 2005Sep 4 2005

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume7 VOLS
ISSN (Print)0589-1019

Other

Other2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
CountryChina
CityShanghai
Period9/1/059/4/05

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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