Improved Medical Image Segmentation Algorithm Based on Laplacian Level Set
一种改进的拉普拉斯水平集医学图像分割算法

Being a key procedure of image recognition and image understanding, image segmentation, on one hand, is regarded as being of important potential value, hence a lot of algorithms have been proposed, on the other hand, it has encountered a lot of challenges. Among all these challenges, one of them is how to acquire continuous segmentation result from blurring region. A new medical image segmentation algorithm based on the Lapalacian level set was proposed, and this algorithm combines regional information into speed function to drive the evolution of level set surface. The algorithm utilizes not only the information of image edges and gradient information, but also image region information. The algorithm takes advantage of regional global optimization features meanwhile maintaining the local features of edges.The new proposed algorithm implements effective segmentation of medical images. Compared with the classical level set segmentation methods, the improved algorithm has good performance in maintaining the continuity of the edges, so that the segmentation result is relatively complete. This algorithm can provide reliable scientific data for image analysis.