Department of Computer Science and Engineering, National Institute of Technology Calicut, Calicut, India.
J Appl Clin Med Phys. 2011 Mar 3;12(3):3285. doi: 10.1120/jacmp.v12i3.3285.
In most of the approaches of computer-aided detection of breast cancer, one of the preprocessing steps applied to the mammogram is the removal/suppression of pectoral muscle, as its presence within the mammogram may adversely affect the outcome of cancer detection processes. Through this study, we propose an efficient automatic method using the watershed transformation for identifying the pectoral muscle in mediolateral oblique view mammograms. The watershed transformation of the mammogram shows interesting properties that include the appearance of a unique watershed line corresponding to the pectoral muscle edge. In addition to this, it is observed that the pectoral muscle region is oversegmented due to the existence of several catchment basins within the pectoral muscle. Hence, a suitable merging algorithm is proposed to combine the appropriate catchment basins to obtain the correct pectoral muscle region. A total of 84 mammograms from the mammographic image analysis database were used to validate this approach. The mean false positive and mean false negative rates, obtained by comparing the results of the proposed approach with manually-identified (ground truth) pectoral muscle boundaries, respectively, were 0.85% and 4.88%. A comparison of the results of the proposed method with related state-of-the-art methods shows that the performance of the proposed approach is better than the existing methods in terms of the mean false negative rate. Using Hausdorff distance metric, the comparison of the results of the proposed method with ground truth shows low Hausdorff distances, the mean and standard deviation being 3.85 ± 1.07 mm.
在大多数计算机辅助乳腺癌检测方法中，应用于乳房 X 光片的预处理步骤之一是去除/抑制胸肌，因为其存在于乳房 X 光片中可能会对癌症检测过程的结果产生不利影响。通过这项研究，我们提出了一种使用分水岭变换识别中侧斜位乳房 X 光片中胸肌的有效自动方法。乳房 X 光片的分水岭变换具有有趣的特性，包括出现与胸肌边缘对应的独特分水岭线。除此之外，还观察到由于胸肌内存在多个集水盆地，胸肌区域被过度分割。因此，提出了一种合适的合并算法来合并适当的集水盆地，以获得正确的胸肌区域。该方法共使用了来自乳腺图像分析数据库的 84 张乳房 X 光片进行验证。通过将所提出方法的结果与手动识别（地面实况）的胸肌边界进行比较，分别获得的平均假阳性和平均假阴性率分别为 0.85%和 4.88%。将所提出方法的结果与相关的最先进方法进行比较表明，在所提出方法的平均假阴性率方面，其性能优于现有方法。使用 Hausdorff 距离度量，将所提出方法的结果与地面实况进行比较显示出较低的 Hausdorff 距离，平均值和标准差分别为 3.85±1.07mm。
