Unsupervised Segmentation of Medical Image Based on Maximizing Mutual Information
基于最大互信息量的图像自动优化分割

Most threshold-based segmentation algorithms rely on the information of the gray level of the original image,without taking account of the spatial information. In this paper a new segmentation method is proposed,in which K-means algorithm is combined with mutual information (MI) technique. The initial threshold can be chosen by using K-means algorithm,and in the iteration process,an optimal threshold will be determined by maximizing the MI between the original and the segmented image. We evaluate the effectiveness of the proposed approach by applying it to the segmentation of medical images and license plate images. The experimental results indicate that the new method has visually better segmentation effect.