Automatic BI-RADS description of mammographic masses

Authors:
Fabián Narváez;Gloria Díaz;Eduardo Romero
Affiliations:
Bioingenium Research Group, Departament of Medicine, National University of Colombia, Bogotá, Colombia;Bioingenium Research Group, Departament of Medicine, National University of Colombia, Bogotá, Colombia;Bioingenium Research Group, Departament of Medicine, National University of Colombia, Bogotá, Colombia
Venue:
IWDM'10 Proceedings of the 10th international conference on Digital Mammography
Year:
2010

Citing 3
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Abstract

This paper presents a CBIR (Content Based Information Retrieval) framework for automatic description of mammographic masses according to the well known BI-RADS lexicon Unlike other approaches, we do not attempt to segment masses but instead, we describe the regions an expert selects, after the series of rules defined in the BI-RADS lexicon The content based retrieval strategy searches similar regions by automatically computing the Mahalanobis distance of feature vectors that describe main shape and texture characteristics of the selected regions A description of a test region is based on the BI-RADS description associated to the retrieved regions The strategy was assessed in a set of 444 masses with different shapes and margins Suggested descriptions were compared with a ground truth already provided by the data base, showing a precision rate of 82.6% for the retrieval task and a sensitivity rate of 80% for the annotation task.