A Study on Digital Mammography: Microcalcification Detection and Classification based on Wavelets

The second leading cause of cancer deaths in women is Breast cancer. Presently mammography is the best available technique for early detection of breast cancer. The common breast abnormalities are masses and calcifications that may be the indications of breast cancer. Some other signs which may lead to breast cancer are bilateral asymmetry and architectural distortion. Previous history had shown that radiologists can miss the detection of a significant proportion of abnormalities because of their wide range of features while reading large amount of mammographic images provided in screening programs. To reduce the chances of missing the detection and to provide an accurate diagnosis, CAD systems are developed. These systems reduce the number of false positives. The present paper is an overview of image processing techniques that are developed for detecting microcalcifications and then classify the microcalcifications