Image similarity and asymmetry to improve computer-aided detection of breast cancer

Authors:
Dave Tahmoush;Hanan Samet
Affiliations:
Computer Science Department, Center for Automation Research, Institute for Advanced Computer Science, University of Maryland, College Park;Computer Science Department, Center for Automation Research, Institute for Advanced Computer Science, University of Maryland, College Park
Venue:
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Year:
2006

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Abstract

An improved image similarity method is introduced to recognize breast cancer, and it is incorporated into a computer-aided breast cancer detection system through Bayes Theorem. Radiologists can use the differences between the left and right breasts, or asymmetry, in mammograms to help detect certain malignant breast cancers. Image similarity is used to determine asymmetry using a contextual and then a spatial comparison. The mammograms are filtered to find the most contextually significant points, and then the resulting point set is analyzed for spatial similarity. We develop the analysis through a combination of modeling and supervised learning of model parameters. This process correctly classifies mammograms 84% of the time, and significantly improves the accuracy of a computer-aided breast cancer detection system by 71%.