Abstract
Mammography is a type of radiography used on the breasts as screening method for women. The indicators for breast cancer aremasses and calcifications. Breast cancer screenings show that radiologists miss 8%–20% of the tumors. For this reason, development of systems for computer-aided detection (CAD) and computer-aided diagnosis (CADx) algorithms is the concern of a lot of researches currently being done. CAD and CADx algorithms assist radiologists in the decision between follow up and biopsy phases. An intelligent Image Processing Technique employed in systems that can help the radiology in detecting abnormal masses. This paper presents a general framework for mammography that will provide advantages for managing information and simplifying process in each layer for imaging technique. A method has been developed to make supporting tools used a framework as a reference model. This method will automatically segment and detect abnormal masses in analog and digital mammography images and compare between results.
Highlights
Breast cancer ranks as the most common form of cancer and second-leading cause of cancer death among women (O.Whi-Vin et al, 2009)
The goal of mammography is the early detection of breast cancer, typically through detection of characteristic masses and/or microcalcifications (Jasjit & Rangaraj, 2006)
The computer-aided detection (CAD) in this paper is validated by using a benchmark database and advanced local database
Summary
Breast cancer ranks as the most common form of cancer and second-leading cause of cancer death among women (O.Whi-Vin et al, 2009). Detection of clustered microcalcifications in mammographic X-ray images in spite of absence of masses helps in diagnosis of early breast cancer. A variety of screening techniques have been developed to improve the accuracy of interpretation Radiologists can improve their performance; with the advances of digital image processing; with computer-aided diagnosis (CAD) system. Imaging techniques play an important role on mammogram images, especially in abnormal areas that cannot be physically felt but can be seen or processed on an analog mammogram or with ultrasound (National Cancer Institute (NCI) Web site) By it appears the importance of developing a framework as a reference model for researchers to implement their techniques, without development overheads in computer-based diagnostic systems and improve drawbacks associated with this imaging technique.
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