The segmentation of a digital mammogram into breast region and film background is a necessary prerequisite in the computer-assisted diagnosis of mammograms. The segmentation method should be robust enough to handle a wide variety of digital mammograms obtained from different image acquisition systems. In addition, the large size of digitized mammograms requires an efficient image processing sys...

The main goal of this paper is to compare performance of connected component labeling algorithms on grayscale digital mammograms. This study was carried out as a part of a research for improving efficiency and accuracy of diagnosing breast cancer using digital mammograms. Three connected component labeling algorithms developed by Jung-Me Park [8], Kenji Suzuki [16] and Kesheng Wu [9], were used...

Breast skin–air interface and pectoral muscle segmentation are usually first steps in all CAD applications on scanned as well as digital mammograms. Breast skin–air interface segmentation is much more difficult task when performed on scanned mammograms than on digital mammograms. In case of pectoral muscle segmentation, segmentation difficulty of analog and digital mammograms is usually similar...

Breast cancer is a significant health problem for women globally; however, timely detection can reduce female morbidity and mortality. Early breast screening has become imperative all women, though, adequate facilities are necessarily required in developing countries like Pakistan, where leading cause of death. To encounter this chronic disease, various image processing techniques have been int...

Computer methodologies are being developed to assist radiologists, as second readers, in the interpretation of mammograms. This could represent further amelioration by increasing diagnostic accuracy in the screening programs. We have developed a computerized scheme to detect clustered microcalcifications in digital mammograms, using 100 mammograms that were randomly selected from the mammograph...

Pectoral muscle identification is often required for breast cancer risk analysis, such as estimating breast density. Traditional methods are overwhelmingly based on manual visual assessment or straight line fitting for the pectoral muscle boundary, which are inefficient and inaccurate since pectoral muscle in mammograms can have curved boundaries. This paper proposes a novel and automatic pecto...

Breast cancer produces a high rate of mortality worldwide. Early diagnosis is essential for treatment, however it is difficult to analyse high density breast tissues. Computer-aided diagnosis systems have been proposed to classify the density of mammograms, having as a major challenge to define the features that better represent the images to be classified. In this study, besides comparing them...

In this paper, we propose a new hybrid system for microcalcifications detection in digital mammograms, using the combination of the CDF 9/7 filter bank and a successive approximations threshold. Microcalcifications are low contrast samples and only have a few pixels in diameter which are difficult to detect. We shown that microcalcifications have not only support in high frequency regions, but ...

In this paper, we present a novel approach to the problem of computer aided analysis of digital mammograms for breast cancer detection: namely, the development of algorithms to recognize unequivocally normal mammograms. First, we eliminate amorphous “clouds” or “blobs” in mammograms produced by normal glandular tissue of varying density using local average subtraction. Then we identify and remo...

Breast cancer is leading cause of death among female cancer patient. However the early detection of breast cancer is dependent on both radiologist’s ability to read mammograms and the quality of mammograms images. Mammography is the effective technique for the screening of breast cancer and abnormalities detection. Screening is one of the key factor to reduce the death rates. The strong correla...