Segmentation of medical images with Canny operator and GVF snake model
Jinyong Cheng, Ruojuan Xue, Wenpeng Lü, Ruixiang Jia · 2008
In computer vision, edge detection is a hot research area in which Canny operator is a typical algorithm. Canny operator has preferable anti-noise ability. However the edge based on Canny operator is not consecutive. GVF Snake model is used widely in image segmentation. But there are problems in convergence processing to boundaries of some medical image because of noise. This paper presents a new segmentation algorithm to medical image. First, rough edge is got by Canny operator, and then thinning method based on mathematical morphology is adopted to get edge map as foundation of GVF Snake model. This method solves the problem that the edge based on Canny operator is not consecutive. And it improves GVF Snake model’s anti-noise ability. Experiments indicate that the new algorithm can improve snake model’s ability to segment the complicated image.