A new global shape prior for level set based segmentation

The global shape prior knowledge has been exploited by many image segmentation approaches in order to improve segmentation results when there are such problems as occlusion, cluttering, low contrast edges, etc. We propose a global shape prior representation and incorporate it into a level set based segmentation framework. This global shape prior can effectively help remove the cluttered elongate structures and island-like artifacts in the segmentation. We experimentally compare the performance of our global shape prior with an extensively used global shape prior introduced in [3]. The experimental results show that our global shape prior averagely achieves 15% higher precision rates with comparable recall rates, which demonstrates the efficacy of the proposed shape prior.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Lei Zhang, Qiang Ji
• Keywords: Computer Vision, Global Shape, ICPR 2008, Shape Prior Knowledge, Shape Prior Representation