中文摘要：

作为乳腺癌计算机辅助诊断系统的重要环节，肿块分割的结果严重影响到肿块良恶性的判别．针对现有方法的不足，本文提出了一种基于简化型脉冲耦合神经网络和改进型矢量无边缘活动轮廓模型的乳腺x射线肿块分割方法．首先，通过数学分析计算SPCNN的相关参数与终止条件，进而利用SPCNN模型分割出肿块的初始轮廓．然后，针对传统CV模型的不足，进行相应的修正得到改进型矢量CV模型．最后，结合SPCNN分割出的初始轮廓，利用改进型的矢量CV模型处理R01分割出肿块．采用北京大学人民医院乳腺中心提供的临床图像以及DDSM数据库的图像进行对比实验，实验结果表明，本文方法相比较现有方法分割结果更为准确，尤其是在处理东方女性致密性案例时，本文方法更有优势．

英文摘要：

Mass segmentation plays an important role in computer-aided diagnosis （CAD） system. The segmentation result seriously affects classifying mass as benign and malignant. By combining the simplified pulse coupled neural network （SPCNN） and the improved vector active contour without edge （vector-CV）, a novel method of mass segmentation in mammogram is proposed in this paper. First, the parameters and termination conditions of SPCNN are obtained through mathematical analysis and the initial contour is segmented by SPCNN. Then, the vector CV model is accordingly modified to overcome the shortcomings of traditional CV model. Finally, combined with the initial contour, the improved vector-CV is used to segment the mass contour. The experiments implemented on the public digital database for screening mammography （DDSM） and the clinical images which are provided by the Center of Breast Disease of Peking University People＇s Hospital indicate that the proposed method is better than the existing methods, especially when dealing with the dense breasts of Oriental female.