Computer-assisted diagnosis of mammographic masses using an information-theoretic image retrieval scheme with BIRADs-based relevance feedback

Georgia D. Tourassi; Carey E. Floyd Jr.

doi:10.1117/12.534090

12 May 2004 Computer-assisted diagnosis of mammographic masses using an information-theoretic image retrieval scheme with BIRADs-based relevance feedback

Georgia D. Tourassi, Carey E. Floyd Jr.

Author Affiliations +

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.534090
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

The purpose of the study was to develop and evaluate a content-based image retrieval (CBIR) approach for computer-assisted diagnosis of masses detected in screening mammograms. The system follows an information theoretic retrieval scheme with a BIRADS-based relevance feedback (RF) algorithm. Initially, a knowledge databank of 365 mammographic regions of interest (ROIs) was created. They were all 512x512 pixel ROIs extracted from DDSM mammograms digitized using the Lumisys digitizer. The ROIs were extracted around the known locations of the annotated masses. Specifically, there were 177 ROIs depicting a biopsy-proven malignant mass and 188 ROIs with a benign mass. Subsequently, the CBIR algorithm was implemented using mutual information (MI) as the similarity metric for image retrieval. The CBIR algorithm formed the basis of a knowledge-based CAD system. Given a databank of mammographic masses with known pathology, a query mass was evaluated. Based on their information content, all similar masses in the databank were retrieved. A relevance feedback algorithm based on BIRADS findings was implemented to determine the relevance factor of the retrieved masses. Finally, a decision index was calculated using the query's k best matches. The decision index effectively combined the similarity metric of the retrieved cases and their relevance factor into a prediction regarding the malignancy status of the mass depicted in the query ROI. ROC analysis was to evaluate diagnostic performance. Performance improved dramatically with the incorporation of the relevance feedback algorithm. Overall, the CAD system achieved ROC area index AZ= 0.86±0.02 for the diagnosis of masses in screening mammograms.

Citation Download Citation

Georgia D. Tourassi and Carey E. Floyd Jr. "Computer-assisted diagnosis of mammographic masses using an information-theoretic image retrieval scheme with BIRADs-based relevance feedback", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.534090

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