Cheng H D, Lui Y M, Freimanis R I
Department of Computer Science, Utah State University, Logan 84322, USA.
IEEE Trans Med Imaging. 1998 Jun;17(3):442-50. doi: 10.1109/42.712133.
Breast cancer continues to be a significant public health problem in the United States. Approximately, 182,000 new cases of breast cancer are diagnosed and 46,000 women die of breast cancer each year. Even more disturbing is the fact that one out of eight women in the United States will develop breast cancer at some point during her lifetime. Since the cause of breast cancer remains unknown, primary prevention becomes impossible. Computer-aided mammography is an important and challenging task in automated diagnosis. It has great potential over traditional interpretation of film-screen mammography in terms of efficiency and accuracy. Microcalcifications are the earliest sign of breast carcinomas and their detection is one of the key issues for breast cancer control. In this study, a novel approach to microcalcification detection based on fuzzy logic technique is presented. Microcalcifications are first enhanced based on their brightness and nonuniformity. Then, the irrelevant breast structures are excluded by a curve detector. Finally, microcalcifications are located using an iterative threshold selection method. The shapes of microcalcifications are reconstructed and the isolated pixels are removed by employing the mathematical morphology technique. The essential idea of the proposed approach is to apply a fuzzified image of a mammogram to locate the suspicious regions and to interact the fuzzified image with the original image to preserve fidelity. The major advantage of the proposed method is its ability to detect microcalcifications even in very dense breast mammograms. A series of clinical mammograms are employed to test the proposed algorithm and the performance is evaluated by the free-response receiver operating characteristic curve. The experiments aptly show that the microcalcifications can be accurately detected even in very dense mammograms using the proposed approach.
乳腺癌在美国仍然是一个重大的公共卫生问题。每年大约有18.2万例新的乳腺癌病例被诊断出来，4.6万名女性死于乳腺癌。更令人不安的是，美国每八名女性中就有一人在其一生中的某个时候会患上乳腺癌。由于乳腺癌的病因仍然不明，一级预防变得不可能。计算机辅助乳腺X线摄影是自动诊断中的一项重要且具有挑战性的任务。在效率和准确性方面，它比传统的胶片-屏片乳腺X线摄影解释具有更大的潜力。微钙化是乳腺癌的最早迹象，其检测是乳腺癌控制的关键问题之一。在本研究中，提出了一种基于模糊逻辑技术的微钙化检测新方法。微钙化首先根据其亮度和不均匀性进行增强。然后，通过曲线检测器排除无关的乳腺结构。最后，使用迭代阈值选择方法定位微钙化。微钙化的形状被重建，并且通过采用数学形态学技术去除孤立像素。所提出方法的基本思想是应用乳腺X线照片的模糊化图像来定位可疑区域，并将模糊化图像与原始图像交互以保持保真度。所提出方法的主要优点是即使在乳腺组织非常致密的乳腺X线照片中也能检测到微钙化。使用一系列临床乳腺X线照片来测试所提出的算法，并通过自由响应接收器操作特征曲线评估性能。实验恰当地表明，使用所提出的方法即使在乳腺组织非常致密的乳腺X线照片中也能准确检测到微钙化。
