Image Segmentation And Object Tracking Of Computer-aided Diagnosis Outside The Anal Canal Disease

Diseases outside the anal canal are common and frequently-occurring diseases while the special nature of the disease and privacy of location make it difficult to treat. Combined with computer image processing technology, the author has made use of computer image processing to study image segmention and object tracking of Computer Aided Diagnosis of the images outside the anal canal in this paper.Firstly, the author has analyzed a variety of typical images of hemorrhoids and feature extraction methods through a large number of the images outside the anal canal.Secondly, the author has investigated the image segmentation technology of the diseases outside the anal canal. As the complexity of human anatomic structure, the abnormity of tissue shape and the difference among individuals, race, and ethnicity in the word, the commonly image segmentation methods is not fit for the medical image. The availability method must be found to resolve the problem. The investigating statuses in quo and developments about medical image segmentation in domestic and foreign fields are reviewed and summarized. What is more, according to medical image characteristic and application demand and combining the texture image, the morphology operation, the partial standard deviation filter with the rangefilt filter, we use texture filter to achieve image segmentation. We guarantee the integrity of the image segmentation, enhancing the division image effective and retaining the image detail at the same time.Finally, the author has tracked the target site for anorectal diseases. Considering diseases parts of the privacy, the author has deeply researched the algorithms of mean shift among many object tracking algorithms. The algorithm of mean shift is a non-parametric method based upon climbing gradient. It searches object by iteration to realize object tracking. In fourth chapter, the algorithm of mean shift theory and its using in object tracking are deduced and described in detail. The application of object tracking about the mean shift algorithm is analyzed through a large number of experiments and good tracking performances of the mean shift algorithm can be achieved.