In this study, a mammographic mass retrieval platform was established using content-based image retrieval method to extract and to model the semantic content of mammographic masses. Specifically, the shape and margin of a mass was classified into different categories, which were sorted by radiologist experts according to BI-RADS descriptors. Mass lesions were analyzed by the likelihoods of each category with defined features including third order moments, curvature scale space descriptors, compactness, solidity, and eccentricity, etc. To evaluate the performance of the retrieval system, we defined that a retrieved image is considered relevant if it belongs to the same class (benign or malignant) as the query image. A total of 476 biopsy-proven mass cases (219 malignant and 257 benign) were used for 10 random test/train partitions. For each test query mass, 5 most similar masses were retrieved from the image library. The performance of the retrieval system was evaluated by ROC analysis of the malignancy rating of the query masses in the test set relative to the biopsy truth. Through 10 random test/train partitions, we found that the averaged area under the ROC curve (Az) was 0.80±0.06. With another independent dataset containing 415 cases (244 malignant and 171 benign) as a test set, the ROC analysis indicated the performance of the retrieval system had an Az of 0.75±0.03.

Date Published: 9 March 2011
PDF: 6 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79632H (9 March 2011); doi: 10.1117/12.878274

Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers M.D.; Bram van Ginneken, Editor(s)