Chan H P, Sahiner B, Lam K L, Petrick N, Helvie M A, Goodsitt M M, Adler D D
Department of Radiology, University of Michigan, Ann Arbor 48109, USA.
Med Phys. 1998 Oct;25(10):2007-19. doi: 10.1118/1.598389.
We are developing computerized feature extraction and classification methods to analyze malignant and benign microcalcifications on digitized mammograms. Morphological features that described the size, contrast, and shape of microcalcifications and their variations within a cluster were designed to characterize microcalcifications segmented from the mammographic background. Texture features were derived from the spatial gray-level dependence (SGLD) matrices constructed at multiple distances and directions from tissue regions containing microcalcifications. A genetic algorithm (GA) based feature selection technique was used to select the best feature subset from the multi-dimensional feature spaces. The GA-based method was compared to the commonly used feature selection method based on the stepwise linear discriminant analysis (LDA) procedure. Linear discriminant classifiers using the selected features as input predictor variables were formulated for the classification task. The discriminant scores output from the classifiers were analyzed by receiver operating characteristic (ROC) methodology and the classification accuracy was quantified by the area, Az, under the ROC curve. We analyzed a data set of 145 mammographic microcalcification clusters in this study. It was found that the feature subsets selected by the GA-based method are comparable to or slightly better than those selected by the stepwise LDA method. The texture features (Az = 0.84) were more effective than morphological features (Az = 0.79) in distinguishing malignant and benign microcalcifications. The highest classification accuracy (Az = 0.89) was obtained in the combined texture and morphological feature space. The improvement was statistically significant in comparison to classification in either the morphological (p = 0.002) or the texture (p = 0.04) feature space alone. The classifier using the best feature subset from the combined feature space and an appropriate decision threshold could correctly identify 35% of the benign clusters without missing a malignant cluster. When the average discriminant score from all views of the same cluster was used for classification, the Az value increased to 0.93 and the classifier could identify 50% of the benign clusters at 100% sensitivity for malignancy. Alternatively, if the minimum discriminant score from all views of the same cluster was used, the Az value would be 0.90 and a specificity of 32% would be obtained at 100% sensitivity. The results of this study indicate the potential of using combined morphological and texture features for computer-aided classification of microcalcifications.
我们正在开发计算机化的特征提取和分类方法，以分析数字化乳腺X线片中的恶性和良性微钙化。设计了描述微钙化大小、对比度、形状及其在簇内变化的形态学特征，用于表征从乳腺X线片背景中分割出的微钙化。纹理特征源自于在包含微钙化的组织区域的多个距离和方向上构建的空间灰度依赖（SGLD）矩阵。使用基于遗传算法（GA）的特征选择技术从多维特征空间中选择最佳特征子集。将基于GA的方法与基于逐步线性判别分析（LDA）过程的常用特征选择方法进行比较。使用选定特征作为输入预测变量的线性判别分类器被制定用于分类任务。通过接收器操作特征（ROC）方法分析分类器输出的判别分数，并通过ROC曲线下的面积Az量化分类准确率。在本研究中，我们分析了一个包含145个乳腺X线微钙化簇的数据集。发现基于GA的方法选择的特征子集与逐步LDA方法选择的特征子集相当或略好。在区分恶性和良性微钙化方面，纹理特征（Az = 0.84）比形态学特征（Az = 0.79）更有效。在组合的纹理和形态学特征空间中获得了最高的分类准确率（Az = 0.89）。与单独在形态学（p = 0.002）或纹理（p = 0.04）特征空间中的分类相比，这种改进具有统计学意义。使用来自组合特征空间的最佳特征子集和适当决策阈值的分类器可以正确识别35%的良性簇，而不会遗漏恶性簇。当使用同一簇所有视图的平均判别分数进行分类时，Az值增加到0.93，并且分类器可以在100%的恶性敏感度下识别50%的良性簇。或者，如果使用同一簇所有视图的最小判别分数，则Az值将为0.90，并且在100%的敏感度下将获得32%的特异性。本研究结果表明了使用组合形态学和纹理特征进行微钙化计算机辅助分类的潜力。
