Improvement of mammographic mass characterization using spiculation measures and morphological features
Improvement of mammographic mass characterization using spiculation measures and morphological features
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DOI：
10.1118/1.1381548
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发表时间：
2001-07-01
期刊：
影响因子：
3.8
通讯作者：
Hadjiiski, LM
中科院分区：
文献类型：
--
作者：
Sahiner, B;Chan, HP;Hadjiiski, LM
我们正在开发新的计算机视觉技术，用于在乳房X光片上表征乳房肿块。我们以前开发了一种基于纹理特征的表征方法。本工作的目标是改进我们的表征方法，利用形态特征。为了实现这一目标，我们开发了一个全自动的，三阶段分割方法，包括聚类，活动轮廓，毛刺检测阶段。分割后，提取描述肿块形状的形态学特征。纹理特征也被提取从一个带的像素周围的质量。在形态、纹理和组合特征空间中采用逐步特征选择和线性判别分析进行分类器设计。使用受试者工作特征曲线下的面积A（z）评价分类准确性。使用了包含102例患者的249张电影的数据集。当应用留一例法将数据集划分为训练者和测试者时，在形态、纹理和组合特征空间中，对单个乳腺摄影视图上的肿块进行分类的任务的平均测试A（z）分别为0.83 +/- 0.02、0.84 +/- 0.02和0.87 +/- 0.02。通过在分类中补充纹理特征与形态特征所获得的改善具有统计学显著性（p = 0.04）。为了将肿块分类为恶性或良性，我们将来自肿块不同视图的留一例判别评分结合起来以获得汇总评分。在该任务中，使用组合特征空间的测试A（z）值为0.91 +/- 0.02。我们的研究结果表明，结合纹理特征与形态特征提取的自动分割的肿块边界将是一种有效的方法，用于计算机辅助表征乳腺肿块。(C)2001年美国医学物理学家协会。
We are developing new computer vision techniques for characterization of breast masses on mammograms. We had previously developed a characterization method based on texture features. The goal of the present work was to improve our characterization method by making use of morphological features. Toward this goal, we have developed a fully automated, three-stage segmentation method that includes clustering, active contour, and spiculation detection stages. After segmentation, morphological features describing the shape of the mass were extracted. Texture features were also extracted from a band of pixels surrounding the mass. Stepwise feature selection and linear discriminant analysis were employed in the morphological, texture, and combined feature spaces for classifier design. The classification accuracy was evaluated using the area A(z) under the receiver operating characteristic curve. A data set containing 249 films from 102 patients was used. When the leave-one-case-out method was applied to partition the data set into trainers and testers, the average test A(z) for the task of classifying the mass on a single mammographic view was 0.83 +/- 0.02, 0.84 +/- 0.02, and 0.87 +/- 0.02 in the morphological, texture, and combined feature spaces, respectively. The improvement obtained by supplementing texture features with morphological features in classification was statistically significant (p = 0.04). For classifying a mass as malignant or benign, we combined the leave-one-case-out discriminant scores from different views of a mass to obtain a summary score. In this task, the test A(z) value using the combined feature space was 0.91 +/- 0.02. Our results indicate that combining texture features with morphological features extracted from automatically segmented mass boundaries will be an effective approach for computer-aided characterization of mammographic masses. (C) 2001 American Association of Physicists in Medicine.