To solve the problem of false positive in micro-calcification detection

a two-layer support vector classifier model with rejection feature(TLSVCRF) is proposed.Firstly the first layer of support vector classifier(SVC) with maximum margin between two classes will be used for classifying the input pattern;then the sphere support vectors of true micro-calcification points to describe the distribution of the sample were obtained by searching all the sphere boundaries containing the samples of this class.Then the input pattern of no-object classes could be rejected by the second support vector domain description(SVDD).Lastly the results of SVC and SVDD classifier are integrated to obtain the right results.Experimental results demonstrate that the method of two-Layer support vector classifier with rejection feature performs better in achieving lower false positive.