An improved asymmetry measure to detect breast cancer

Radiologists can use the differences between the left and right breasts, or asymmetry, in mammograms to help detect certain malignant breast cancers. An image similarity method has been improved to make use of this knowledge base to recognize breast cancer. Image similarity is determined using computer-aided detection (CAD) prompts as the features, and then a cluster comparison is done to determine whether there is asymmetry. We develop the analysis through a combination of clustering and supervised learning of model parameters. This process correctly classifies cancerous mammograms 95% of the time, and all mammograms 84% of the time, and thus asymmetry is a measure that can play an important role in significantly improving computer-aided breast cancer detection systems. This technique represents an improvement in accuracy of 121% over commercial techniques on non-cancerous cases. Most computer-aided detection (CAD) systems are tested on images which contain cancer on the assumption that images without cancer would produce the same number of false positives. However, a pre-screening system is designed to remove the normal cases from consideration, and so the inclusion of a pre-screening system into CAD dramatically reduces the number of false positives reported by the CAD system. We define three methods for the inclusion of pre-screening into CAD, and improve the performance of the CAD system by over 70% at low levels of false positives.