Estimating the level of false positives in detection of microcalcifications in mammogram images
In computerized detection of clustered microcalcifications (MCs) from mammogram images the occurrence of false positives (FPs) varies greatly from case to case. In this work, we develop a probabilistic modeling approach to estimate the number of individual FPs present in a detected MC lesion. We describe the number of true positives (TPs) by a Poisson-Binomial probability distribution, wherein a logistic regression model is trained to determine the probability for a detected MC to be a TP based on its characteristics in the detector output. We demonstrate the proposed approach on a set of 188 full-field digital mammography images with two existing MC detectors [difference-of-Gaussians (DoG) and SVM detector]. The results show that the error level in the estimated number of FPs can be as low as 2.51 on average when the number of FPs is as high as 11.38 in a detected MC lesion.