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.
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Narváez, F., Díaz, G., Romero, E. (2010). Automatic BI-RADS Description of Mammographic Masses. In: Martí, J., Oliver, A., Freixenet, J., Martí, R. (eds) Digital Mammography. IWDM 2010. Lecture Notes in Computer Science, vol 6136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13666-5_91
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DOI: https://doi.org/10.1007/978-3-642-13666-5_91
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