In the last few years, computerized tool play important role in detection of breast cancer. This paper proposes a method for breast cancer diagnosis in digital mammograms using GLCM (Grey Level Co-occurrence Matrix) features. In this paper CAD (Computer Aided Diagnosis) system developed using GLCM feature and neural network. Mammography is an efficient tool for early detection of breast cancer....

To assess the correlation between breast arterial calcifications (BAC) on digital mammography and the extent of coronary artery disease (CAD) diagnosed with dual source coronary computed tomography angiography (CTA) in a population of women both symptomatic and asymptomatic for coronary artery disease. 100 consecutive women (aged 34 - 86 years) who underwent both coronary CTA and digital mammog...

Introduction: Most studies in mammography have only been carried out in a comparison of image quality and lesion detection in film-screen mammography (FSM) and full field digital mammography (FFDM) systems. The aim of this paper was to evaluate mean glandular dose (MGD) of patient and affecting factors by FSM and FFDM to estimate which one gives a brief radiation protection to patient. Material...

This chapter presents neural network-based techniques for the classification of micro-calcification patterns in digital mammograms. Artificial neural network (ANN) applications in digital mammography are mainly focused on feature extraction, feature selection, and classification of micro-calcification patterns into ‘benign’ and ‘malignant’. An extensive review of neural network based techniques...

Innovation in breast imaging techniques is leading to better breast cancer detection. Concurrently, breast cancer screening recommendations are changing, and breast density is becoming a more significant factor in breast cancer screening. Digital mammography has become the preferred screening method, with more breast imaging centers including the use of digital breast tomosynthesis (DBT). The t...

Because of the lack of mammography databases with a large amount of codified images and identified characteristics like pathology, type of breast tissue, and abnormality, there is a problem for the development of robust systems for computer-aided diagnosis. Integrated to the Image Retrieval in Medical Applications (IRMA) project, we present an available mammography database developed from the u...