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 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...

In this study we present a pre-CAD system that aims to help the radiologists in the analysis of the high number of mammograms that they have to evaluate each day, helping to prevent the increased number of misclassification that could happen, due to the repetitive task to which they are submitted. The method consists in extracting features from mammograms, previously classified by experts accor...

We contribute with a publicly available repository of digital mammograms including both raw and preprocessed images. The study of mammographies is the most used and effective method to diagnose breast cancer. It is possible to improve quality of images for more accurate predictions of radiologists, by applying some preprocessing techniques. In this work we introduce a method for mammogram prepr...

Digital mammograms are coupled with noise which makes de-noising a challenging problem. In the literature, few wavelets like daubechies db3 and haar have been used for de-noising medical images. However, wavelet filters such as sym8, daubechies db4 and coif1 at certain level of soft and hard threshold have not been taken into account for mammogram images. Therefore, in this study five wavelet f...

Microcalcification (MC) clusters in mammograms can be an indicator of breast cancer. In this work we propose for the first time the use of support vector machine (SVM) learning for automated detection of MCs in digitized mammograms. In the proposed framework, MC detection is formulated as a supervised-learning problem and the method of SVM is employed to develop the detection algorithm. The pro...