Lee S, Lo C, Wang C, Chung P, Chang C, Yang C, Hsu P
Department of Radiology, Taichung Veterans General Hospital, 40705, Taichung, Taiwan, ROC.
Int J Med Inform. 2000 Oct;60(1):29-57. doi: 10.1016/s1386-5056(00)00067-8.
This paper presents a prototype of a computer-aided design (CAD) diagnostic system for mammography screening to automatically detect and classify microcalcifications (MCCs) in mammograms. It comprises four modules. The first module, called the Mammogram Preprocessing Module, inputs and digitizes mammograms into 8-bit images of size 2048x2048, extracts the breast region from the background, enhances the extracted breast and stores the processed mammograms in a data base. Since only clustered MCCs are of interest in providing a sign of breast cancer, the second module, called the MCCs Finder Module, finds and locates suspicious areas of clustered MCCs, called regions of interest (ROIs). The third module, called the MCCs Detection Module, is a real time computer automated MCCs detection system that takes as inputs the ROIs provided by the MCCs Finder Module. It uses two different window sizes to automatically extract the microcalcifications from the ROIs. It begins with a large window of size 64x64 to quickly screen mammograms to find large calcified areas, this is followed by a smaller window of size 8x8 to extract tiny, isolated microcalcifications. Finally, the fourth module, called the MCCs Classification Module, classifies the detected clustered microcalcifications into five categories according to BI-RADS (Breast Imaging Reporting and Data System) format recommended by the American College of Radiology. One advantage of the designed system is that each module is a separate component that can be individually upgraded to improve the whole system. Despite that it is still is a prototype system a preliminary clinical evaluation at TaiChung Veterans General Hospital (TCVGH) has shown that the system is very flexible and can be integrated with the existing Picture Archiving and Communications System (PACS) currently implemented in the Department of Radiology at TCVGH.
本文介绍了一种用于乳腺钼靶筛查的计算机辅助设计（CAD）诊断系统原型，该系统可自动检测和分类乳腺钼靶片中的微钙化（MCC）。它由四个模块组成。第一个模块称为乳腺钼靶预处理模块，将乳腺钼靶片输入并数字化为2048x2048大小的8位图像，从背景中提取乳腺区域，增强提取的乳腺并将处理后的乳腺钼靶片存储在数据库中。由于只有簇状微钙化才是提供乳腺癌迹象的关键，因此第二个模块称为微钙化发现模块，用于查找和定位簇状微钙化的可疑区域，即感兴趣区域（ROI）。第三个模块称为微钙化检测模块，是一个实时计算机自动化微钙化检测系统，它将微钙化发现模块提供的ROI作为输入。它使用两种不同的窗口大小从ROI中自动提取微钙化。首先使用64x64的大窗口快速筛查乳腺钼靶片以找到大的钙化区域，然后使用较小窗口8x8提取微小的、孤立的微钙化。最后，第四个模块称为微钙化分类模块，根据美国放射学会推荐的BI-RADS（乳腺影像报告和数据系统）格式将检测到的簇状微钙化分为五类。所设计系统的一个优点是每个模块都是一个单独的组件，可以单独升级以改进整个系统。尽管它仍然是一个原型系统，但在台中荣民总医院（TCVGH）进行的初步临床评估表明，该系统非常灵活，可以与TCVGH放射科目前实施的现有图像存档和通信系统（PACS）集成。
