Computer aided detection of breast masses on prior mammograms

An important purpose of a CAD system is that it can serve as a second reader to alert radiologists to subtle cancers that may be overlooked. In this study, we are developing new computer vision techniques to improve the detection performance for subtle masses on prior mammograms. A data set of 159 patients containing 318 current mammograms and 402 prior mammograms was collected. A new technique combining gradient field analysis with Hessian analysis was developed to prescreen for mass candidates. A suspicious structure in each identified location was initially segmented by seed-based region growing and then refined by using an active contour method. Morphological, gray level histogram and run-length statistics features were extracted. Rule-based and LDA classifiers were trained to differentiate masses from normal tissues. We randomly divided the data set into two independent sets; one set of 78 cases for training and the other set of 81 cases for testing. With our previous CAD system, the case-based sensitivities on prior mammograms were 63%, 48% and 32% at 2, 1 and 0.5 FPs/image, respectively. With the new CAD system, the case-based sensitivities were improved to 74%, 56% and 35%, respectively, at the same FP rates. The difference in the FROC curves was statistically significant (p<0.05 by AFROC analysis). The performances of the two systems for detection of masses on current mammograms were comparable. The results indicated that the new CAD system can improve the detection performance for subtle masses without a trade-off in detection of average masses.