Joint parametric and non-parametric curve evolution for medical image segmentation

This paper proposes a new joint parametric and nonparametric curve evolution algorithm of the level set functions for medical image segmentation. Traditional level set algorithms employ non-parametric curve evolution for object matching. Although matching image boundaries accurately, they often suffer from local minima and generate incorrect segmentation of object shapes, especially for images with noise, occlusion and low contrast. On the other hand, statistical model-based segmentation methods allow parametric object shape variations subject to some shape prior constraints, and they are more robust in dealing with noise and low contrast. In this paper, we combine the advantages of both of these methods and jointly use parametric and non-parametric curve evolution in object matching. Our new joint curve evolution algorithm is as robust as and at the same time, yields more accurate segmentation results than the parametric methods using shape prior information. Comparative results on segmenting ventricle frontal horn and putamen shapes in MR brain images confirm both robustness and accuracy of the proposed joint curve evolution algorithm.