By Topic

Vector-valued Mumford-Shah model with nonlinear statistical shape prior for image segmentation

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
$33 $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

6 Author(s)
Guocai Liu ; College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China ; Maofa Xiao ; Zhihao Yu ; Weili Yang
more authors

In order to effectively segment complex medical images, the narrow band level set of shape prior was mapped into its kernel space by a nonlinear kernel function, then the Principal Component Analysis (PCA) was performed in the kernel space so as to obtain its base vectors, and nonlinear statistical shape prior can be integrated into a vector-valued Mumford-Shah model. The experimental results show that the proposed model is effective and practicable for the segmentation of the low-contrast optic disk obscured partly by blood vessels in colour optic nerve head images of early-stage glaucoma patients.

Published in:

Complex Medical Engineering (CME), 2011 IEEE/ICME International Conference on

Date of Conference:

22-25 May 2011