70. Computer aided detection system for the automated classification of clustered microcalcifications in digital mammograms. (December 2018) - Massafra, R.; Fanizzi, A.; Basile, T.; Bellotti, R.; Carbonara, R.; Forgia, D. La; Moschetta, M.; Tamborra, P.; Tangaro, S.; Losurdo, L.; Didonna, V.
Abstract : Purpose: Mammography is the most effective and low-cost method for the early detection of breast cancers. The aim of this study was to develop an automated model in order to improve detection and characterization of clustered microcalcifications on full-field digital mammograms (FFDM). Methods: We analyzed 98 digital mammograms extracted from the public database Breast Cancer Digital Repository with 276 microcalcification clusters; these images were previously evaluated in double blind by two radiologists which had identified the microcalcification clusters. We identified some regions of interest (ROI) on mammograms after pre-processing by using the Circular Hough Transform algorithm in order to recognize circles corresponding to the microcalcifications. Then, we selected morphological and morphometric features for each ROI, such as Speeded Up Robust Feature, as well as classic statistical features based on Haar Transform. These features were used for giving instructions to a Random Forest classifier in order to recognize – riga clustered microcalcifications normal and abnormal ROIs. Finally, we tested the performance of – riga this automated model in cross-validation and with statistical measurements. Results: Statistical results (Table 1 ) showed good performance of proposed model in automatic detection of breast clustered microcalcifications on FFDM (sensitivity over 90% with 3.54 false positives – riga per image), even in young patients with dense breast tissue (sensitivity over 88%) and according to published data[1] . Also classification performance of clustered microcalcifications (AUC 98, 6 ± 0, 1, Accuracy 97, 6 ± 0, 2%, Sensitivity 97, 7 ± 0, 4%, Specificity 96, 2 ± 0, 4%) was comparable with the state of the art[2] . Conclusions: The proposed automated model enables to improve detection of clustered microcalcifications that could be characterized by difficult diagnostic interpretation especially when occurred in dense breast parenchyma. The proposed model results highly performing and comparable to the state-of-art approaches.