Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces
Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces
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DOI：
10.1118/1.598389
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发表时间：
1998-10-01
期刊：
影响因子：
3.8
通讯作者：
Adler, DD
中科院分区：
文献类型：
--
作者：
Chan, HP;Sahiner, B;Adler, DD
我们正在开发计算机特征提取和分类方法，以分析数字化乳房X线照片上的恶性和良性微钙化。描述微钙化的大小、对比度和形状及其在聚类中的变化的形态学特征被设计用于表征从乳房X线摄影背景分割的微钙化。纹理特征来自于从包含微钙化的组织区域在多个距离和方向上构建的空间灰度依赖（SGLD）矩阵。采用基于遗传算法的特征选择技术从多维特征空间中选择最佳特征子集。基于遗传算法的方法进行了比较，常用的特征选择方法的基础上逐步线性判别分析（LDA）程序。线性判别分类器使用选定的功能作为输入预测变量制定的分类任务。通过受试者工作特征（ROC）方法分析分类器输出的判别分数，并通过ROC曲线下面积A（z）量化分类准确性。在这项研究中，我们分析了145个乳腺摄影微钙化簇的数据集。结果发现，由基于GA的方法选择的特征子集是可比的或略优于逐步LDA方法选择的那些。纹理特征（A（z）= 0.84）比形态特征（A（z）= 0.79）更能有效区分微钙化的良恶性。最高的分类精度（A（z）= 0.89），在联合纹理和形态特征空间。与单独的形态学（p = 0.002）或纹理（p = 0.04）特征空间中的分类相比，该改善具有统计学显著性。使用组合特征空间中的最佳特征子集和适当的决策阈值的分类器可以正确识别35%的良性集群，而不会错过恶性集群。当来自相同聚类的所有视图的平均判别分数用于分类时，A（z）值增加到0.93，并且分类器可以以100%的恶性灵敏度识别50%的良性聚类。或者，如果使用来自同一聚类的所有视图的最小判别分数，则A（z）值将为0.90，并且在100%灵敏度下将获得32%的特异性。本研究的结果表明，使用相结合的形态和纹理特征的微钙化的计算机辅助分类的潜力。(C)1998年美国医学物理学家协会。[S0094-2405（98）00910-9]。
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, A(z), 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 (A(z) = 0.84) were more effective than morphological features (A(z) = 0.79) in distinguishing malignant and benign microcalcifications. The highest classification accuracy (A(z) = 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 ail views of the same cluster was used for classification, the A(z) 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 A(z) 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. (C) 1998 American Association of Physicists in Medicine. [S0094-2405(98)00910-9].