A computer-aided design mammography screening system for detection and classification of microcalcifications

被引:47
|
作者
Lee, SK
Lo, CS
Wang, CM
Chung, PC
Chang, CI [1 ]
Yang, CW
Hsu, PC
机构
[1] Univ Maryland Baltimore Cty, Dept Comp Sci & Elect Engn, Remote Sensing Signal & Image Proc Lab, Baltimore, MD 21250 USA
[2] Taichung Vet Gen Hosp, Dept Radiol, Taichung 40705, Taiwan
[3] Natl Def Med Ctr, Dept Diagnost Radiol, Taipei 100, Taiwan
[4] Chung Shan Med & Dent Coll, Dept Radiol, Taichung 402, Taiwan
[5] Natl Cheng Kung Univ, Dept Elect Engn, Tainan 70101, Taiwan
[6] Taichung Vet Gen Hosp, Ctr Comp, Taichung, Taiwan
关键词
classification; computer-aided design (CAD) diagnostic system; detection; mammography; microcalcifications (MCCs); Mammogram Preprocessing Module; MCCs Classification Module; MCCs Detection Module; MCCs Finder Module; Nijmegen database; shape cognitron (S-Cognitron); TaiChung Veterans General Hospital (TCVGH);
D O I
10.1016/S1386-5056(00)00067-8
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 2048 x 2048, 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 64 x 64 to quickly screen mammograms to find large calcified areas, this is followed by a smaller window of size 8 x 8 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. (C) 2000 Elsevier Science Ireland Ltd. All rights reserved.
引用
收藏
页码:29 / 57
页数:29
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