Spatial distribution modeling for detection of clustered microcalcifications

We propose a spatial point-process modeling approach to improve the detection of clustered microcalcifications (MCs) in mammogram images. Apart from the predominant approach for MC detection, in which individual MCs in an image are first detected independently and then grouped into clusters, our proposed approach aims to incorporate the spatial clustering property of the MCs directly into the detection process (i.e., MCs tend to appear in small clusters). We model the MCs by a marked point process (MPP) in which spatially neighboring MCs are interactive with each other. The detection is achieved through maximum a posteriori (MAP) estimation of the parameters of the MPP model. The proposed approach was evaluated with a dataset of 141 clinical mammograms, and the results show that it could yield improved performance compared with a recently proposed SVM detector.