By Topic

Local Region Based Medical Image Segmentation Using J-Divergence Measures

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Wanlin Zhu ; Inst. of Autom., Chinese Acad. of Sci. ; Tianzi Jiang ; Xiaobo Li

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

Published in:

Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the

Date of Conference:

2005