title: A computer-aided design mammography screening system for detection and classification of microcalcifications.
abstract: 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.
authors.affiliation: Department of Radiology, Taichung Veterans General Hospital, 40705, Taichung, Taiwan, ROC.
authors.forename: S
authors.initials: S
authors.lastname: Lee
authors.forename: C
authors.initials: C
authors.lastname: Lo
authors.forename: C
authors.initials: C
authors.lastname: Wang
authors.forename: P
authors.initials: P
authors.lastname: Chung
authors.forename: C
authors.initials: C
authors.lastname: Chang
authors.forename: C
authors.initials: C
authors.lastname: Yang
authors.forename: P
authors.initials: P
authors.lastname: Hsu
mesh_terms: D000465:Algorithms; D001941:Breast Diseases / Q000145:classification / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000145:classification / Q000000981:diagnostic imaging*; D017076:Computer-Aided Design; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008403:Mass Screening / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted