Abstract

This paper proposes a new joint parametric and nonparamet- ric curve evolution algorithm of the level set functions for medical image segmentation. Traditional level set algorithms employ non-parametric curve evolution for ob ject matching. Although matching image bound- aries accurately, they often suffer from local minima and generate in- correct segmentation of ob ject shapes, especially for images with noise, occlusion and low contrast. On the other hand, statistical model-based segmentation methods allow parametric ob ject shape variations sub ject 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 ob ject matching. Our new joint curve evolution algorithm is as robust as and at the same time, yields more accurate segmenta- tion 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.