Support Vector Machine And Its Applications In Medical Image Segmentation

Medical image segmentation is an important and difficult issue in medical image processing. Theperformance of traditional pattern classification methods, which are based on the principle ofExperiential Risk Minimization, achieve the best, only when the number of training samples approachesinfinity. Unfortunately, the number of training samples is actually limited and the data dimension is high,thus the performance of traditional pattern classification algorithms is deteriorated in medical imagesegmentation. Taken into account the good generalization of support vector machine in small samples,nonlinearity and high dimension space and features of medical images, this dissertation deeply studiessupport vector machine methods and their application in medical image segmentation. The maincontributions of this thesis are given below. 1. For the numbered samples by the interactive mode in medical image segmentation, theprecondition of infinity for traditional pattern classification methods can not be satisfied. Given theadvantages of the good generalization for support vector machine in the small-sample, and the dispersefeature of the segmented objects in medical images, support vector machine is used to performsegmentation of medical images. The brain tissues are classified from the stimulant MR images.Experiment results show that the SVM classifier offers lower computational time and betterclassification precision than the BP and the FCM methods. The comparative experiments are made usingthe different number of training samples and the different scans, and it confirms that SVM method holdsthe better classification ability in the small-sample. 2. Considering that the image features extracted from the medical images can be well characterizedby the Gaussian function, and the successful applications of the Gaussian function in other fields, wechoose the Gaussian function as the kernel for segmentation in medical images. Furthermore, given thefact that the optimal discriminative function is determined by the support vectors, and the supportvectors are centered as the Gaussian function, we put forward an effective algorithm which provides theparameter of Gaussian kernel using support vectors, and it solves a difficult problem for the parameter ofGaussian kernel in application. 3. Two groups of image features, the combined features both textures and gray features, and graylevel features based on window region, are studied. 6 textures statistics features based on theco-occurrence matrix of gray level (namely, contrast, correlation, sum average, sum variance, sum IVentropy and difference entropy) and 3 gray features (namely, the pixel intensity, the median filterintensity and the average intensity of window size of each pixel) are chosen as the image combinedfeatures both texture and gray in the brain tissues classification. The gray level values of the pixels andthe pixel intensities in the neighborhood are used for gray level features extraction. Taking into accountthe disadvantage about the square window region in extracting the gray level features, and the slicksurface among the segmented objects in medical images, we design a new method for extracting the graylevel features based on the circle window region. The gray level features own the better classificationability and the lower time. 4. Since SVM is very sensitive to outliers and noises in the training set and the fuzzy feature existsin medical images, we hereby studied fuzzy support vector machine based on the affinity among samples.The fuzzy membership is defined by not only the relation between a sample and its cluster center, butalso the affinity among samples. A method defining the affinity among samples is proposed using asphere with minimum volume while containing maximum of the samples. Then, the fuzzy membershipis defined according to the position of samples in sphere space, which distinguished between the val...