Multiscale level set image segmenting method based on kernel fuzzy clustering

The invention discloses a multiscale level set image segmenting method based on kernel fuzzy clustering. The method includes step 1 adopting a divide marking method to calculate the gray average of small areas; 2 adopting the gray average to initialize a membership matrix, conducting kernel fuzzy clustering to obtain the initial contour of the interested area; 3 designing an edge constraint stop item of the multiscale level set; 4 conducting iteration evolution to segment an image. The method overcomes the shortcoming that over-segmenting is easy to cause for the divide based on edge information, and edge segmenting missing is easy to cause for the C-V model level set method based on area information for the images with unclear edges and poor contrast. The edge information and the area information are effectively blended by adopting the kernel fuzzy clustering method, the multiscale edge constraint stop item is added, the re-initialization is eliminated, the segmenting accuracy is improved, and the real-time performance of the algorithm is ensured.