Varela C, Timp S, Karssemeijer N
Department of Radiology, Radboud University, Nijmegen Medical Centre, The Netherlands.
Phys Med Biol. 2006 Jan 21;51(2):425-41. doi: 10.1088/0031-9155/51/2/016. Epub 2006 Jan 4.
We are developing a new method to characterize the margin of a mammographic mass lesion to improve the classification of benign and malignant masses. Towards this goal, we designed features that measure the degree of sharpness and microlobulation of mass margins. We calculated these features in a border region of the mass defined as a thin band along the mass contour. The importance of these features in the classification of benign and malignant masses was studied in relation to existing features used for mammographic mass detection. Features were divided into three groups, each representing a different mass segment: the interior region of a mass, the border and the outer area. The interior and the outer area of a mass were characterized using contrast and spiculation measures. Classification was done in two steps. First, features representing each of the three mass segments were merged into a neural network classifier resulting in a single regional classification score for each segment. Secondly, a classifier combined the three single scores into a final output to discriminate between benign and malignant lesions. We compared the classification performance of each regional classifier and the combined classifier on a data set of 1076 biopsy proved masses (590 malignant and 486 benign) from 481 women included in the Digital Database for Screening Mammography. Receiver operating characteristic (ROC) analysis was used to evaluate the accuracy of the classifiers. The area under the ROC curve (A(z)) was 0.69 for the interior mass segment, 0.76 for the border segment and 0.75 for the outer mass segment. The performance of the combined classifier was 0.81 for image-based and 0.83 for case-based evaluation. These results show that the combination of information from different mass segments is an effective approach for computer-aided characterization of mammographic masses. An advantage of this approach is that it allows the assessment of the contribution of regions rather than individual features. Results suggest that the border and the outer areas contained the most valuable information for discrimination between benign and malignant masses.
我们正在开发一种新方法来表征乳腺钼靶肿块病变的边缘，以改善良性和恶性肿块的分类。为实现这一目标，我们设计了一些特征来测量肿块边缘的锐利程度和微叶状特征。我们在肿块的边界区域计算这些特征，该边界区域被定义为沿着肿块轮廓的一条细带。结合用于乳腺钼靶肿块检测的现有特征，研究了这些特征在良性和恶性肿块分类中的重要性。特征被分为三组，每组代表不同的肿块部分：肿块的内部区域、边界和外部区域。使用对比度和毛刺测量来表征肿块的内部和外部区域。分类分两步进行。首先，将代表三个肿块部分的特征合并到一个神经网络分类器中，从而为每个部分得出一个单一的区域分类分数。其次，一个分类器将这三个单一分数合并为最终输出，以区分良性和恶性病变。我们在来自乳腺钼靶筛查数字数据库的481名女性的1076个活检证实的肿块（590个恶性和486个良性）数据集上比较了每个区域分类器和组合分类器的分类性能。使用受试者操作特征（ROC）分析来评估分类器的准确性。对于肿块内部部分，ROC曲线下面积（A(z)）为0.69，边界部分为0.76，外部部分为0.75。对于基于图像的评估，组合分类器的性能为0.81，基于病例的评估为0.83。这些结果表明，来自不同肿块部分的信息组合是计算机辅助表征乳腺钼靶肿块的有效方法。这种方法的一个优点是它允许评估区域的贡献而不是单个特征。结果表明，边界和外部区域包含了区分良性和恶性肿块的最有价值的信息。
