Medical Images Analysis Based On Watershed Algorithm

Image segmentation stands for representing an image as a set of connected regions. In the fields of image processing, segmentation attracts most of the attentions. It is the foundation of image understanding, pattern recognition and object-based image compression. This technique has made a profound effect on other image processing fields. At present, there are many methods trying to solve this problem, some of them are based on the characteristic of certain images, and some are general methods. From the two aspects of theoretical study and practical application, this dissertation pays more emphasis on the segmenting images with different structures, and implements two novel algorithms to segment images with different structures, i.e. extracting of curvilinear structure and improved watershed transform.Retinal images have many tiny structures, which are capillary vessels. In order to extract the curvilinear structure from these images, this dissertation provides an algorithm, which calculates the maximum of every pixel gradient values in different directions, and then compares the maximum with a given threshold to decide whether the pixel is part of the curvilinear structure or not. During the extracting, The heavy noise created by the extraction process is reduced by the application of mathematical morphology.Watershed segmentation based on mathematical morphology is a popular tool for image processing. It is easy and intuitive, rapidly computational and parallelizable. It is sensitive of obscure boundary, and can get a single-pixel boundary which has connected and closed profile. But there is much over segmentation in the results of the method. With minute analysis...