Cerebral Artery Segmentation with Level Set Methods

In this paper we present the application of two level set methods (active contour without edges and fast marching) to the problem of segmenting arteries from medical images. The two methods fit in the same Hamilton-Jacobi type equation but their underlying mechanisms are quite different. The first method finds the objects of interest using an energy minimization equation without the information of image edges. The second method segments an image by evaluating its time-cross-map, which is calculated from the image gradient. We implement the first method from scratch, and use a medical imaging library (ITK) for the second. Their segmentation results and performances are compared. Each method’s strengths and weaknesses are also analyzed.