4 March 2011 Multi-view information fusion for automatic BI-RADS description of mammographic masses
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Most CBIR-based CAD systems (Content Based Image Retrieval systems for Computer Aided Diagnosis) identify lesions that are eventually relevant. These systems base their analysis upon a single independent view. This article presents a CBIR framework which automatically describes mammographic masses with the BI-RADS lexicon, fusing information from the two mammographic views. After an expert selects a Region of Interest (RoI) at the two views, a CBIR strategy searches similar masses in the database by automatically computing the Mahalanobis distance between shape and texture feature vectors of the mammography. The strategy was assessed in a set of 400 cases, for which the suggested descriptions were compared with the ground truth provided by the data base. Two information fusion strategies were evaluated, allowing a retrieval precision rate of 89.6% in the best scheme. Likewise, the best performance obtained for shape, margin and pathology description, using a ROC methodology, was reported as AUC = 0.86, AUC = 0.72 and AUC = 0.85, respectively.
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Fabián Narvaez, Gloria Díaz, Eduardo Romero, "Multi-view information fusion for automatic BI-RADS description of mammographic masses", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79630A (4 March 2011); doi: 10.1117/12.878392; https://doi.org/10.1117/12.878392

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