Image segmentation by changing template block by block

In this paper, an entropy-based image segmentation method is proposed to segment a gray-scale image. The method starts with an arbitrary template. An index called Gray-scale Image Entropy (GIE) is employed to measure the degree of resemblance between the template and the underlying true scene that gives rise to the gray-scale image. The classification status of a block of pixels in the template is modified in a way to maximize the GIE. By repeatedly processing all blocks of pixels until a termination condition is met, the template would be changed to a configuration that closely resembles the true scene. This optimum template (in an entropy sense) is taken to be the desired segmented image. Investigation results from simulation study and the segmentation of practical images demonstrate the feasibility of the proposed method.