为克服医学图像微钙化点检测中假阳性高的缺点,构造了一种带拒识能力的双层支持向量模型分类器,用于钙化点检测.检测时,首先利用基于最大间隔超平面的支持向量分类器（SVC）对输入模式进行分类判决;然后通过求取真实钙化点样本特征空间最小的包含球形边界来得到钙化点样本的球形支持向量域表示（SVDD）;接着利用钙化点的支持向量域表示对输入模式进行拒识或接受处理;最后利用SVC与SVDD两个分类器的结果来进行综合判决.仿真实验结果表明,该算法在不影响微钙化点的检出率的情况下,可部分解决假阳性高的问题.

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.