Edge Points Based Feature Extraction Method For Medical Image Classification

Along with the wide applications of all kinds of medical devices, medical image processing technology is playing a more and more important part in medical science research and clinical medicine. It ensures the clinician a more direct and clearer observation of patient's internal pathological change organs, increasing the accurate diagnosis rate. Therefore, domestic and foreign experts pay great attention to the technology. Medical image based computer aided diagnosis (MIBCAD) is rapidly developed. MIBCAD could help radiologist raise the accurate diagnosis rate, assist doctors to diagnose and identify medical images. Pattern classification based on feature extraction is an important step of MIBCAD. For this reason, the feature extraction and classification of medical image are mainly studied in this thesis.Because the points on the edge can show the characteristic of an image, an edge point detection method is proposed in this thesis. It simplifies the image data so that the typical points representing the image can be found. Image edge detection is also a key technology of image processing. Watershed transform and fuzzy C-means (FCM) clustering method together are applied to detect edge points. In this way, not only is the "over-segmentation problem" of watershed transform eliminated, but also the speed of FCM clustering algorithm which is recursively called is boosted. Experimental results display the utility of proposed methods.For feature extraction, since the quality of feature extraction is a crucial element of the performance of classification, choosing a proper way to extract features of image is extremely important. There are a lot of ways to describe image features, such as color, texture, and shape. Because edge points have directionality, and can describe image features more accurately comparing with other ways, accordingly the method of calculating the neighborhood's orientation information measure is applied for feature extraction in this thesis.For classification, supported vector machine (SVM) is imported, which is a machine learning method. Firstly, the theoretical principle and mathematical model of SVM, especially the popularizing ability and kernel function theory are analyzed. Then the feature extraction results are taken as input and SVM is applied to classify the images of the image data base and the choice of kernels and parameters are discussed. Plentiful theoretical analyses and experiments along with contrast experiments demonstrate the proposed edge points based feature extraction for medical image classification has satisfying classification results.