Medical Image Segmentation Based on an Improved 2D Entropy

Medical image segmentation is the basis of medical image three-dimension reconstruction. The accuracy of image segmentation directly affects the results of image 3D reconstruction. Medical image is a kind of grayscale image. In order to adequately utilize gray information and spatial information of image, the traditional 2D gray histogram is improved and forms the 2D D-value attribute gray histogram. Computation method of average gray and 2D entropy is improved. Use spatial information as a substitute for gray probability to compute entropy. Computation of entropy is based on D-value attribute gray histogram and created spatial different attribute information entropy (SDAIVE). In experiment, a series of head CT images are segmented. Experimental results show that improved threshold method can better segment noise image. This method has strong anti-noise capability and clear segmentation results.