Ganesan Karthikeyan, Acharya Rajendra U, Chua Chua K, Min Lim C, Mathew Betty, Thomas Abraham K
Department of ECE, Ngee Ann Polytechnic, Singapore, Singapore.
Proc Inst Mech Eng H. 2013 Jul;227(7):721-32. doi: 10.1177/0954411913480669. Epub 2013 Mar 26.
Mammograms are by far one of the most preferred methods of screening for breast cancer. Early detection of breast cancer can improve survival rates to a greater extent. Although the analysis and diagnosis of breast cancer are done by experienced radiologists, there is always the possibility of human error. Interobserver and intraobserver errors occur frequently in the analysis of medical images, given the high variability between every patient. Also, the sensitivity of mammographic screening varies with image quality and expertise of the radiologist. So, there is no golden standard for the screening process. To offset this variability and to standardize the diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. This article presents a classification pipeline to improve the accuracy of differentiation between normal, benign, and malignant mammograms. Several features based on higher-order spectra, local binary pattern, Laws' texture energy, and discrete wavelet transform were extracted from mammograms. Feature selection techniques based on sequential forward, backward, plus-l-takeaway-r, individual, and branch-and-bound selections using the Mahalanobis distance criterion were used to rank the features and find classification accuracies for combination of several features based on the ranking. Six classifiers were used, namely, decision tree classifier, fisher classifier, linear discriminant classifier, nearest mean classifier, Parzen classifier, and support vector machine classifier. We evaluated our proposed methodology with 300 mammograms obtained from the Digital Database for Screening Mammography and 300 mammograms from the Singapore Anti-Tuberculosis Association CommHealth database. Sensitivity, specificity, and accuracy values were used to compare the performances of the classifiers. Our results show that the decision tree classifier demonstrated an excellent performance compared to other classifiers with classification accuracy, sensitivity, and specificity of 91% for the Digital Database for Screening Mammography database and 96.8% for the Singapore Anti-Tuberculosis Association CommHealth database.
乳房X光检查是目前最常用的乳腺癌筛查方法之一。早期发现乳腺癌可以在很大程度上提高生存率。尽管乳腺癌的分析和诊断由经验丰富的放射科医生进行，但始终存在人为误差的可能性。鉴于每个患者之间存在很大差异，在医学图像分析中，观察者间和观察者内误差经常发生。此外，乳房X光筛查的敏感性因图像质量和放射科医生的专业水平而异。因此，筛查过程没有黄金标准。为了抵消这种变异性并使诊断程序标准化，人们正在努力开发用于乳腺癌图像诊断和分级的自动化技术。本文提出了一种分类流程，以提高正常、良性和恶性乳房X光片鉴别诊断的准确性。从乳房X光片中提取了基于高阶谱、局部二值模式、劳斯纹理能量和离散小波变换的多个特征。使用基于顺序向前、向后、加1减1、个体以及基于马氏距离准则的分支定界选择的特征选择技术对特征进行排序，并根据排序结果找到多个特征组合的分类准确率。使用了六种分类器，即决策树分类器、费舍尔分类器、线性判别分类器、最近均值分类器、帕曾分类器和支持向量机分类器。我们使用从数字乳腺筛查数据库获得的300张乳房X光片和新加坡防痨协会社区健康数据库的300张乳房X光片对我们提出的方法进行了评估。使用敏感性、特异性和准确性值来比较分类器的性能。我们的结果表明，与其他分类器相比，决策树分类器表现出色，在数字乳腺筛查数据库中的分类准确率、敏感性和特异性为91%，在新加坡防痨协会社区健康数据库中为96.8%。
