Point process modeling for determining detection accuracy of mammographic microcalcifications
The occurrence of false positives (FPs) varies greatly from case to case in computerized detection of clustered microcalcifications (MCs) from mammogram images. In this work, we explore using a stochastic modeling approach to estimate the number of individual FPs present in a detected MC lesion. We model the spatial occurrence of FPs in the MC detector output by a Poisson spatial point process, of which the parameters are estimated from the detected objects both in the lesion region and in a reference region in the immediate neighborhood of the lesion. We demonstrate the proposed approach on a set of 188 full-field digital mammography images with two existing MC detectors. The results show that on average the error level in the estimated number of FPs is 3.62 when the actual number of FPs is at the level of 11.38 in a detected MC lesion.