A level set-based global shape prior and its application to image segmentation

Global shape prior knowledge is a special kind of semantic information that can be incorporated into an image segmentation process to handle the difficulties caused by such problems as occlusion, cluttering, noise, and/or low contrast boundaries. In this work, we propose a global shape prior representation and incorporate it into a level set based image segmentation framework. This global shape prior can effectively help remove the cluttered elongate structures and island-like artifacts from the evolving contours. We apply this global shape prior to segmentation of three sequences of electron tomography membrane images. The segmentation results are evaluated both quantitatively and qualitatively by visual inspection. Accurate segmentation results are achieved in the testing sequences, which demonstrates the capability of the proposed global shape prior representation.