A Knowledge-based Segmentation Method Integrating both Region and Boundary Information of Medical Images

In this article, the author proposed a hybrid segmentation method which integrates region, boundary and priori knowledge information of medical images. The basic algorithm of this method is level set active contours. The speed function is initialized according to the gradient of the image, and is modified according to statistical characteristic of the segmented regions as the curve evolves. To make the curve stop accurately at the boundary of the object, an energy function is constructed by improving Chan-Vese model. The priori knowledge of the region of interest (ROI) is also integrated into this energy function. The experiment data consists of both simulated images and real clinical images. Precision, accuracy and efficiency are considered in evaluating this method. The evaluation result shows that this method is robust, accurate and has high performance, especially when the boundary is weak or dotted.