Automated selection of BI-RADS lesion descriptors for reporting calcifications in mammograms

Sophie Paquerault; Yulei Jiang; Robert M. Nishikawa; Robert A. Schmidt; Carl J. D'Orsi; Carl J. Vyborny; Gillian M. Newstead

doi:10.1117/12.480847

15 May 2003 Automated selection of BI-RADS lesion descriptors for reporting calcifications in mammograms

Sophie Paquerault, Yulei Jiang, Robert M. Nishikawa, Robert A. Schmidt, Carl J. D'Orsi, Carl J. Vyborny, Gillian M. Newstead

Author Affiliations +

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.480847
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

We are developing an automated computer technique to describe calcifications in mammograms according to the BI-RADS lexicon. We evaluated this technique by its agreement with radiologists' description of the same lesions. Three expert mammographers reviewed our database of 90 cases of digitized mammograms containing clustered microcalcifications and described the calcifications according to BI-RADS. In our study, the radiologists used only 4 of the 5 calcification distribution descriptors and 5 of the 14 calcification morphology descriptors contained in BI-RADS. Our computer technique was therefore designed specifically for these 4 calcification distribution descriptors and 5 calcification morphology descriptors. For calcification distribution, 4 linear discriminant analysis (LDA) classifiers were developed using 5 computer-extracted features to produce scores of how well each descriptor describes a cluster. Similarly, for calcification morphology, 5 LDAs were designed using 10 computer-extracted features. We trained the LDAs using only the BI-RADS data reported by the first radiologist and compared the computer output to the descriptor data reported by all 3 radiologists (for the first radiologist, the leave-one-out method was used). The computer output consisted of the best calcification distribution descriptor and the best 2 calcification morphology descriptors. The results of the comparison with the data from each radiologist, respectively, were: for calcification distribution, percent agreement, 74%, 66%, and 73%, kappa value, 0.44, 0.36, and 0.46; for calcification morphology, percent agreement, 83%, 77%, and 57%, kappa value, 0.78, 0.70, and 0.44. These results indicate that the proposed computer technique can select BI-RADS descriptors in good agreement with radiologists.

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Sophie Paquerault, Yulei Jiang, Robert M. Nishikawa, Robert A. Schmidt, Carl J. D'Orsi, Carl J. Vyborny, and Gillian M. Newstead "Automated selection of BI-RADS lesion descriptors for reporting calcifications in mammograms", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.480847

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KEYWORDS

Mammography

Databases

Computer aided design

Computing systems

Computer aided diagnosis and therapy

Feature extraction

Image segmentation

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