Hough transform for clustered microcalcifications detection in full-field digital mammograms

A. Fanizzi; T. M. A. Basile; L. Losurdo; N. Amoroso; R. Bellotti; U. Bottigli; R. Dentamaro; V. Didonna; A. Fausto; R. Massafra; M. Moschetta; P. Tamborra; S. Tangaro; D. La Forgia

doi:10.1117/12.2273814

19 September 2017 Hough transform for clustered microcalcifications detection in full-field digital mammograms

A. Fanizzi, T. M. A. Basile, L. Losurdo, N. Amoroso, R. Bellotti, U. Bottigli, R. Dentamaro, V. Didonna, A. Fausto, R. Massafra, M. Moschetta, P. Tamborra, S. Tangaro, D. La Forgia

Author Affiliations +

Proceedings Volume 10396, Applications of Digital Image Processing XL; 1039616 (2017) https://doi.org/10.1117/12.2273814
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

Many screening programs use mammography as principal diagnostic tool for detecting breast cancer at a very early stage. Despite the efficacy of the mammograms in highlighting breast diseases, the detection of some lesions is still doubtless for radiologists. In particular, the extremely minute and elongated salt-like particles of microcalcifications are sometimes no larger than 0.1 mm and represent approximately half of all cancer detected by means of mammograms. Hence the need for automatic tools able to support radiologists in their work. Here, we propose a computer assisted diagnostic tool to support radiologists in identifying microcalcifications in full (native) digital mammographic images. The proposed CAD system consists of a pre-processing step, that improves contrast and reduces noise by applying Sobel edge detection algorithm and Gaussian filter, followed by a microcalcification detection step performed by exploiting the circular Hough transform. The procedure performance was tested on 200 images coming from the Breast Cancer Digital Repository (BCDR), a publicly available database. The automatically detected clusters of microcalcifications were evaluated by skilled radiologists which asses the validity of the correctly identified regions of interest as well as the system error in case of missed clustered microcalcifications. The system performance was evaluated in terms of Sensitivity and False Positives per images (FPi) rate resulting comparable to the state-of-art approaches. The proposed model was able to accurately predict the microcalcification clusters obtaining performances (sensibility = 91.78% and FPi rate = 3.99) which favorably compare to other state-of-the-art approaches.

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A. Fanizzi, T. M. A. Basile, L. Losurdo, N. Amoroso, R. Bellotti, U. Bottigli, R. Dentamaro, V. Didonna, A. Fausto, R. Massafra, M. Moschetta, P. Tamborra, S. Tangaro, and D. La Forgia "Hough transform for clustered microcalcifications detection in full-field digital mammograms", Proc. SPIE 10396, Applications of Digital Image Processing XL, 1039616 (19 September 2017); https://doi.org/10.1117/12.2273814

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