Improving uniformity in detection performance of clustered microcalcifications in mammograms
Due to variability among different subjects, the detection accuracy of microcalcifications (MC) in mammograms often varies greatly from case to case. Even for a well-developed MC detector, its performance can be hampered by a number of factors ranging from imaging noise to inhomogeneity in the breast tissue. To address this issue, we use a Bayes' risk approach to account for the variability in the detector output, wherein the probability model of the false-positives (FPs) is determined directly from the case under consideration. In the experiment, we demonstrated the proposed approach on a set of 408 mammograms. The results show that it could both improve the uniformity in detection accuracy among different cases and reduce the FP rate by as much as 44.16% with true-positive rate at 85%.