Study Of Texture Segmentation Method For Medical Images

CBIR (Content Based Image Retrieval) is characterized by the ability of the system to retrieve relevant images based on their visual contents, such as color, texture, shape, rather than by using atomic attributes or keywords assigned to them. Image segmentation is the most important preprocessing technology of CBIR, only through feature extraction and image segmentation can CBIR be implemented successfully.We are always interested in some parts (targets) of a image when we research on it. These targets are corresponding to some special regions in the image. These targets must be extracted from image before we could recognize and analysis them. Image segmentation is the technology and process that divide image into some regions, which have their own characteristics. Image segmentation is the key step from image processing to image analysis, and it is also the foundation of image understanding.In this paper, we make a summary of image segmentation methods firstly, and then recount its applications and research in biomedicine. Secondly, we review the segmentation methods of image integrated with texture information, and then we make a texture analysis on a human liver CT image through texture feature parameter based on co-occurrence matrix. The experiment result shows that these parameters can reflect texture character of different region in human liver CT image. Based on the result, a region-growing algorithm is proposed, through extracting texture parameter that based on co-occurrence matrix from image. This method makes use of gray and texture information. Finally, the method is implemented by program language.The result of experimentation shows the method that is designed in the paper is effective in segmentation of some medical images, and the idea, which integrates many kinds of information into segmentation method, will be an important direction in the research on image segmentation.