Study On Outliers Detection Algorithm In Medical Image Based On FSVM

Research and development of computer-assisted diagnosis system is a hot topic in the area of medical image processing. On the purpose of solving the problem of high false positive rate and low efficiency in microcalcification detection, an improved support vector machine called flexible SVM proposed here is introduced to help the diagnosis, which is extremely meaningful. The job has been done are mainly presented as follows:(1)Aiming at shortcomings of classical SVM, in order to construct a new algorithm with rejection ability and solve the contradiction between training speed and scale of training set as well as contradiction between classification speed and numbers of support vectors, then a new flexible support vector machine is proposed which will be used as classifier for microcalcifications detection.(2)For many reasons, it is necessary to enhance the medical image and increase the contrast between focuses and background. However, general method not only enhances focuses but also normal tissue. Nonlinear gray re-distribution method for image enhancement based on feature of objects is proposed in this paper.(3)In order to improve efficiency of microcalcification detection, Method for region of interest detection based on D-S evidence theory and flexible support vector machine is proposed. A classifier based on D-S evidence theory information fusion is used as a rough detector, which can eliminate many regions that are not of interests, which made the detection algorithm more efficient.(4)To improve the accuracy and efficiency of microcalcification detection, a method based on iterative rank-order filters subspace restricted and FSVM for microcalcification is proposed. Iterative rank-order filter is used as roughdetector to eliminate large numbers of non-microcalcifications and provide more representative negative samples for training of FS VM. Then FS VM can possess better discrimination ability.