Graph cut based deformable model with statistical shape priors

This paper presents a novel graph cut based segmentation approach with shape priors. The model incorporates statistical shape prior information with the active contour without edges model [6]. Our model also relaxes the homogeneity constraint that assumes that the image is modeled by a piecewise constant approximation. The major contribution of this paper is to present a graph cut optimization for the energy function. Hence, the resultant approach is a fully automatic shape based segmentation approach that is insensitive to initialization and does not require any user interaction. Due to the polynomial time complexity of graph cut optimization approaches, our segmentation technique is much faster than the state of the art deformable models segmentation approaches.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Noha Youssry El-Zehiry, Adel Elmaghraby
• Keywords: Computer Vision, Graph Cut, Graph Cut Optimization, ICPR 2008, Segmentation Approach