Automatised detection of microcalcification in mammography. (September 2016)

• Record Type: Journal Article
• Authors: Fanizzi, A.; Tangaro, S.; Bellotti, R.; Basile, T.M.A.; Bottigli, U.; Losurdo, L.; Massafra, R.; Tamborra, P.; Didonna, V.; La Forgia, D.
• Is Part Of: Physica medica. Volume 32(2016)Supplement 3
• Page Start: 217
• Publication Date: 2016-09
• Subjects: Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2016.07.730
• Languages: English
• ISSNs: 1120-1797

Abstract

Abstract : Introduction: An important area in which an improvement of the imaging techniques would be extremely important, is the diagnosis of breast cancer. For this purpose, mammography is the principal diagnostic tool used. Although it is effective in the early detection of breast cancer, exists a real need for new automatic approaches that can improve the accuracy of detection of breast cancer in mammogram Images. In fact, a computerized system as a second reader can support the radiologist in the interpretation of these exams by reducing the number of false positives and thus, the biopsy procedures not necessary. Purpose: In this paper, we propose a Computer Aided Detection System (CAD) for the microcalcification in mammogram images as a diagnostic support tool for radiologists in the analysis. Materials and methods: We develop a fully automated tool for (1) pre-processing images using the edge detection process described by Canny which was designed to be an optimal edge detector according to particular criteria; (2) region of Interest extraction; (3) Adapted Hough Transform to identify the microcalcification cluster. The proposed method was evaluated using cases from publicly available mammography dataset such as Breast Cancer Digital Repository (BCDR) database. Results: We present the results obtained in terms of accuracy, sensitivity, false positive for image. The proposed system shows results comparable state of the art. Conclusion: The proposed method was advantageous in the identification of microcalcifications.