To improve the speed and the problem of high false positive in micro-calcification detection

a novel micro-calcification detection method based on support vector classifier model with rejection feature and Iterative Rank-order Filter Subspace is proposed.In the training step

iterative rank-order filter is used as coarse detector

and the non-micro-calcification training samples are selected from the constrained subspace.So it overcomes the effect of number difference between the negative and positive samples

and at the same time it reduces the computing load in training stage.In the detection step

coarse detection is applied at first to find suspect micro-calcification region

then Firs the first layer of support vector classifier (SVC) with maximum margin between two classes will be used for classification the input pattern;and the sphere support vectors of true micro-calcification points describing the distribution of the sample are obtained by searching all the sphere boundaries containing the samples of this class.So the input pattern of no-object classes can be rejected by the second support vector domain description (SVDD).Lastly the results of SVC and SVDD classifier are integrated to obtain the accurate results.Experimental results demonstrate that the new calcification detection method performs better in achieving lower false positive rate (FPR) and fast speed.