SVM算法及其在乳腺X片微钙化点自动检测中的应用

Principles of SVM and Its Application in Micro-calcifications Detection in Mammogram

支持矢量机(SVM)是一种新的统计学习方法,其学习原则是使结构风险最小,而非经典学习方法所遵循经验风险最小原则.这使得SVM具有更强的泛化能力.并且,由于SVM求解的是凸二次优化问题,使之能保证所找到的极值解就是全局最优解.本文首次将SVM算法用于乳腺X影像微钙化点自动检测中,对临床实际病例的试用结果表明,同目前常用的基于经验风险最小的人工神经网络(ANN)分类方法相比,SVM具有更高的识别率,值得应用推广.

Support vector machine (SVM) is a new statistical learning method. Compared to the classical machine learning methods, the learning discipline of SVM is to minimize the structural risk instead of empirical risk used in the learning discipline of classical methods, and SVM gives better generative performance. Because SVM algorithm is a convex quadratic optimization problem, the local optimal solution is certainly the global optimal one. In this paper, SVM algorithm is applied to detect the micro-calcifications in mammogram for the first time. The algorithm is tested with mammograms of clinical patients and results show that SVM method achieves a more accuracy position in comparison with artificial neural network (ANN) based on the empirical risk minimization, and is valuable for application in clinical engineering.