Improving uniformity in detection performance of clustered microcalcifications in mammograms
Maria V. Sainz de, Yongyi Yang · 2015
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%.