Increased Discrimination in Level Set Methods with Embedded Conditional Random Fields

We propose a novel approach for improving level set seg- mentation methods by embedding the potential functions from a discriminatively trained conditional random field (CRF) into a level set energy function. The CRF terms can be efficiently estimated and lead to both discriminative lo- cal potentials and edge regularizers that take into account interactions among the labels. Unlike discrete CRFs, the use of a continuous level set framework allows the natural use of flexible continuous regularizers such as shape priors. We show promising experimental results for the method on two difficult medical image segmentation tasks.

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Dana Cobzas (University of Alberta), Mark Schmidt (University of British Columbia)
• Keywords: Computer Vision, Conditional Random Fields, CVPR 2009