Medical Image Classification Based On SVM

Currently with the wide application of various image equipment in medicine,medical image based computer aided diagnosis(MIBCAD)has developed rapidly.Computer aided diagnosis(CAD)can improve radiologists'accuracy and help them to identify and classify the medical images quickly and efficiently.One of the most important steps of the MIBCAD is the pattern classification based on feature extraction.Several classification methods such as ANN are based on traditional statistical theory with infinite training samples.But in practice,the number of the sample is limited.Therefore,the traditional methods are inclined to bring many problems like overfitting and local minimum.Vipnik proposed Support Vector Machine(SVM)in 1992-1995,and it is derived from Vapnik-Chervouenkis Dimension theory and Structural Risk Minimization(SRM)principle in Statistical Learning Theory(SLT).This idea balances between learning accuracy of special training samples and model's predicted capability by limited sample information.The improvement of SVM has successfully solved the puzzles in many other machine learning methods,such as overfitting,non-linear,disaster of dimensionality,local minimum,and so on.So SVM is concerned as a new popular research aspect after pattern identification and ANN in machine learning field.In this thesis,SVM as a new machine learning method is brought into medical image classification.The main contributions of this thesis are given as follows:(1)Have realized the application of SVM method in medical image classification.The experiment result based on medical image classification proved the validity of the method.(2)We deeply study Support Vector Machine algorithm,and propose a hybrid model of combing Quantum-behaved Particle Swarm Optimization(QPSO) and SVM. It is applied in medicial image classification.(3)We deeply study K-means algorithm,and propose a hybrid model of combing K-means and SVM, and it is applied in medical image classification.(4) We use Particle Swarm Optimization and Quantum-behaved Particle Swarm Optimization to improve K-means algorithm.We combine the improved K-means and SVM to classify medical image.