Chan H P, Wei D, Helvie M A, Sahiner B, Adler D D, Goodsitt M M, Petrick N
Department of Radiology, University of Michigan, Ann Arbor, USA.
Phys Med Biol. 1995 May;40(5):857-76. doi: 10.1088/0031-9155/40/5/010.
We studied the effectiveness of using texture features derived from spatial grey level dependence (SGLD) matrices for classification of masses and normal breast tissue on mammograms. One hundred and sixty-eight regions of interest (ROIS) containing biopsy-proven masses and 504 ROIS containing normal breast tissue were extracted from digitized mammograms for this study. Eight features were calculated for each ROI. The importance of each feature in distinguishing masses from normal tissue was determined by stepwise linear discriminant analysis. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. We investigated the dependence of classification accuracy on the input features, and on the pixel distance and bit depth in the construction of the SGLD matrices. It was found that five of the texture features were important for the classification. The dependence of classification accuracy on distance and bit depth was weak for distances greater than 12 pixels and bit depths greater than seven bits. By randomly and equally dividing the data set into two groups, the classifier was trained and tested on independent data sets. The classifier achieved an average area under the ROC curve, Az, of 0.84 during training and 0.82 during testing. The results demonstrate the feasibility of using linear discriminant analysis in the texture feature space for classification of true and false detections of masses on mammograms in a computer-aided diagnosis scheme.
我们研究了利用从空间灰度共生矩阵（SGLD）导出的纹理特征对乳腺钼靶片中的肿块和正常乳腺组织进行分类的有效性。本研究从数字化乳腺钼靶片中提取了168个包含活检证实肿块的感兴趣区域（ROI）和504个包含正常乳腺组织的ROI。为每个ROI计算了8个特征。通过逐步线性判别分析确定每个特征在区分肿块与正常组织中的重要性。采用受试者操作特征（ROC）方法评估分类准确性。我们研究了分类准确性对输入特征以及SGLD矩阵构建中的像素距离和比特深度的依赖性。结果发现，其中五个纹理特征对分类很重要。对于大于12像素的距离和大于7比特的比特深度，分类准确性对距离和比特深度的依赖性较弱。通过将数据集随机且均匀地分为两组，在独立数据集上对分类器进行训练和测试。分类器在训练期间的ROC曲线下平均面积Az为0.84，在测试期间为0.82。结果表明，在计算机辅助诊断方案中，在纹理特征空间中使用线性判别分析对乳腺钼靶片中肿块的真假检测进行分类是可行的。
