Microcalcification detection in full-field digital mammograms: A fully automated computer-aided system. (August 2019)

• Record Type: Journal Article
• Authors: Basile, T.M.A.; Fanizzi, A.; Losurdo, L.; Bellotti, R.; Bottigli, U.; Dentamaro, R.; Didonna, V.; Fausto, A.; Massafra, R.; Moschetta, M.; Tamborra, P.; Tangaro, S.; La Forgia, D.
• Is Part Of: Physica medica. Volume 64(2019)
• Page Start: 1
• Page End: 9
• Publication Date: 2019-08
• Subjects: Breast cancer -- Clustered microcalcification detection -- Image processing -- Computer-aided system -- Hough transform; 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.2019.05.022
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Clusters of microcalcifications can be considered as early signs of breast cancer. A three phases approach is proposed: preprocessing-detection-clustering. Application of circular Hough Transform for microcalcification detection. The proposed method reached a sensitivity of 91.78 Abstract: Background: Microcalcification clusters in mammograms can be considered as early signs of breast cancer. However, their detection is a very challenging task because of different factors: large variety of breast composition, highly textured breast anatomy, impalpable size of microcalcifications in some cases, as well as inherent low contrast of mammograms. Thus, the need to support the clinicians' work with an automatic tool. Methods: In this work a three-phases approach for clustered microcalcification detection is presented. Specifically, it is made up of a pre-processing step, aimed at highlighting potentially interesting breast structures, followed by a single microcalcification detection step, based on Hough transform, that is able to grasp the innate characteristic shape of the structures of interest. Finally, a cluster identification step to group microcalcifications is carried out by means of a clustering algorithm able to codify expert domain rules. Results: The detection performance of the proposed method has been evaluated on 364 mammograms of 182 patients obtaining a true positive ratio of 91.78 % with 2.87 false positives per image. Conclusions: Experimental results demonstrated that the proposed method is able to detect microcalcification clusters in digital mammograms showing performance comparable to different methodologies exploited in the state-of-art approaches, with the advantage that it does not require any training phase and a large set of data. The performance of the proposed approach remains high even for more difficult clinical cases of mammograms of young women having high-density breast tissue thus resulting in a reduced contrast between microcalcifications and surrounding dense tissues.