Automatic Categorization of Mammographic Masses Using BI-RADS as a Guidance

In this study, we present a clinically guided technical method for content-based categorization of mammographic masses. Our work is motivated by the continuing effort in content-based image annotation and retrieval to extract and model the semantic content of images. Specifically, we classified the shape and margin of mammographic mass into different categories, which are designated by radiologists according to descriptors from Breast Imaging Reporting and Data System Atlas (BI-RADS). Experiments were conducted within subsets selected from datasets consisting of 346 masses. In the experiments that categorize lesion shape, we obtained a precision of 70% with three classes and 87.4% with two classes. In the experiments that categorize margin, we obtained precisions of 69.4% and 74.7% for the use of four and three classes, respectively. In this study, we intend to demonstrate that this classification based method is applicable in extracting the semantic characteristics of mass appearances, and thus has the potential to be used for automatic categorization and retrieval tasks in clinical applications.