Dept. of Electr. & Comput. Eng., Cincinnati Univ., OH.
IEEE Trans Med Imaging. 1996;15(3):246-59. doi: 10.1109/42.500063.
At present, mammography associated with clinical breast examination and breast self-examination is the only effective and viable method for mass breast screening. The presence of microcalcifications is one of the primary signs of breast cancer. It is, difficult however, to distinguish between benign and malignant microcalcifications associated with breast cancer. Here, the authors define a set of image structure features for classification of malignancy. Two categories of correlated gray-level image structure features are defined for classification of "difficult-to-diagnose" cases. The first category of features includes second-order histogram statistics-based features representing the global texture and the wavelet decomposition-based features representing the local texture of the microcalcification area of interest. The second category of features represents the first-order gray-level histogram-based statistics of the segmented microcalcification regions and the size, number, and distance features of the segmented microcalcification cluster. Various features in each category were correlated with the biopsy examination results of 191 "difficult-to-diagnose" cases for selection of the best set of features representing the complete gray-level image structure information. The selection of the best features was performed using the multivariate cluster analysis as well as a genetic algorithm (GA)-based search method. The selected features were used for classification using backpropagation neural network and parameteric statistical classifiers. Receiver operating characteristic (ROC) analysis was performed to compare the neural network-based classification with linear and k-nearest neighbor (KNN) classifiers. The neural network classifier yielded better results using the combined set of features selected through the GA-based search method for classification of "difficult-to-diagnose" microcalcifications.
目前，与临床乳房检查和乳房自我检查相结合的乳房 X 光检查是大规模乳房筛查的唯一有效可行方法。微钙化的存在是乳腺癌的主要征象之一。然而，要区分与乳腺癌相关的良性和恶性微钙化是很困难的。在这里，作者为恶性肿瘤的分类定义了一组图像结构特征。为了对“难以诊断”的病例进行分类，定义了两类相关的灰度图像结构特征。第一类特征包括基于二阶直方图统计的特征，用于表示全局纹理，以及基于小波分解的特征，用于表示感兴趣的微钙化区域的局部纹理。第二类特征表示分割的微钙化区域的基于一阶灰度直方图的统计量以及分割的微钙化簇的大小、数量和距离特征。每个类别中的各种特征与 191 个“难以诊断”病例的活检检查结果相关联，以选择表示完整灰度图像结构信息的最佳特征集。通过多元聚类分析和基于遗传算法（GA）的搜索方法选择最佳特征。使用反向传播神经网络和参数统计分类器对选择的特征进行分类。进行了接收者操作特征（ROC）分析，以比较基于神经网络的分类与线性和 K-最近邻（KNN）分类器。使用基于 GA 的搜索方法选择的特征进行组合分类时，神经网络分类器对“难以诊断”的微钙化分类的效果更好。
