Abstract: Concerning the low training speed of mammography multi-classification, the Hypersphere Multi-Class Support Vector Data Description (HSMC-SVDD) algorithm was proposed. The Hypersphere One-Class SVDD (HSOC-SVDD) was extended to a HSMC-SVDD as a kind of immediate multi-classification. Through extracting gray-level co-occurrence matrix features of mammography, then Kernel Principle Component Analysis (KPCA) was used to reduce dimension, finally HSMC-SVDD was used for classification. As each category trained only one HSOC-SVDD, its training speed was higher than that of the present multi-class classifiers. The experimental results show that compared with the combined classifier, in which the average train time is 40.2 seconds, proposed by Wei (WEI L Y, YANG Y Y, NISHIKAWA R M,et al.A study on several machine-learning methods for classification of malignant and benign clustered micro-calcifications. IEEE Transactions on Medical Imaging, 2005, 24(3): 371-380), the training time of HSMC-SVDD classifier is 21.369 seconds, the accuracy is up to 76.6929% and it is suitable for solving classification problems of many categories.

Key words: mammograph, multi-class Support Vector Data Description (SVDD), Gray-Level Co-occurrence Matrix (GLCM), Kernel Principle Component Analysis (KPCA)

摘要： 针对乳腺X光医学图像多分类问题中训练速度比较慢的问题，提出超球体多分类支持向量数据描述(HSMC-SVDD)分类算法，即把超球体单分类支持向量数据描述直接扩展到超球体多分类支持向量数据描述。通过对乳腺X光图像提取灰度共生矩阵特征；然后用核主成分分析(KPCA)对数据进行降维；最后用超球体多分类支持向量数据描述分类器进行分类。由于每一类样本只参与构造一个超球体的训练，因此训练速度明显提高。实验结果表明，这种超球体多分类支持向量数据描述分类器的平均训练时间为21.369s，训练时间比Wei等(WEI L Y, YANG Y Y, NISHIKAWA R M,et al.A study on several machine-learning methods for classification of malignant and benign clustered micro-calcifications. IEEE Transactions on Medical Imaging, 2005, 24(3): 371-380)提出的组合分类器（平均训练时间40.2s）减少了10~20s，分类精度最高达76.6929%，适合解决类别数较多的分类问题。

关键词: 乳腺X光图像, 多类支持向量数据描述, 灰度共生矩阵, 核主成分分析