Computerized classification of malignant and benign microcalcifications on mammograms: Texture analysis using an artificial neural network
Computerized classification of malignant and benign microcalcifications on mammograms: Texture analysis using an artificial neural network
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DOI：
10.1088/0031-9155/42/3/008
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发表时间：
1997-03-01
影响因子：
3.5
通讯作者：
Goodsitt, MM
中科院分区：
文献类型：
--
作者：
Chan, HP;Sahiner, B;Goodsitt, MM
我们研究了使用从乳房X线照片中提取的纹理特征来预测微钙化的存在是否与恶性或良性病理相关的可行性。54例患者（良性26例，恶性28例）的86张乳腺X线照片被用作病例样本。所有病变均由乳腺成像专家建议进行手术活检。首先针对低频背景密度变化校正包含微钙化的感兴趣区域（ROI）。从背景校正的ROI中构建轴向和对角方向上十个不同像素距离处的空间灰度依赖（SGLD）矩阵。从每个SGLD矩阵中提取了13个纹理度量。使用逐步特征选择技术，最大限度地分离的两个类分布，从多维特征空间中选择的纹理特征的子集。采用留一法训练和测试了反向传播人工神经网络（ANN）分类器，以识别恶性或良性微钙化簇。用受试者工作特征（ROC）方法对ANN的性能进行了分析。结果发现，六个纹理特征的子集提供了最高的分类精度之间的特征集研究。ANN分类器实现了0.88的ROC曲线下面积。通过设置适当的决策阈值，28例良性病例中有11例被正确识别（特异性39%），而没有错过任何恶性病例（100%灵敏度）。这个初步结果表明，计算机纹理分析可以提取乳房X线摄影信息，是不明显的视觉检查。计算机提取的纹理信息可用于辅助乳房X线摄影解释，具有减少良性病例的活检和提高乳房X线摄影的阳性预测值的潜力。
We investigated the feasibility of using texture features extracted from mammograms to predict whether the presence of microcalcifications is associated with malignant or benign pathology. Eighty-six mammograms from 54 cases (26 benign and 28 malignant) were used as case samples. All lesions had been recommended for surgical biopsy by specialists in breast imaging. A region of interest (ROI) containing the microcalcifications was first corrected for the low-frequency background density variation. Spatial grey level dependence (SGLD) matrices at ten different pixel distances in both the axial and diagonal directions were constructed from the background-corrected ROI. Thirteen texture measures were extracted from each SGLD matrix. Using a stepwise feature selection technique, which maximized the separation of the two class distributions, subsets of texture features were selected from the multi-dimensional feature space. A backpropagation artificial neural network (ANN) classifier was trained and tested with a leave-one-case-out method to recognize the malignant or benign microcalcification clusters. The performance of the ANN was analysed with receiver operating characteristic (ROC) methodology. It was found that a subset of six texture features provided the highest classification accuracy among the feature sets studied. The ANN classifier achieved an area under the ROC curve of 0.88. By setting an appropriate decision threshold, 11 of the 28 benign cases were correctly identified (39% specificity) without missing any malignant cases (100% sensitivity) for patients who had undergone biopsy. This preliminary result indicates that computerized texture analysis can extract mammographic information that is not apparent by visual inspection. The computer-extracted texture information may be used to assist in mammographic interpretation, with the potential to reduce biopsies of benign cases and improve the positive predictive value of mammography.