A parallel algorithm has been developed to detect clustered microcalcifications in digital mammography. Labeling of the image is performed by a deterministic relaxation scheme in which both image data and prior beliefs are weighted simultaneously using a Bayesian scheme. The image data is represented by parameter images representing local contrast and shape. A random field models contextual relations between pixel labels, which enables bringing in prior knowledge about the spatial properties of the structures to be detected. By defining long range interaction between background and calcification labels the detector can be tuned to be more sensitive inside clusters than outside, ensuring that isolated spots will only be interpreted as calcifications if they are in the neighborhood of others. In this paper attention is focused on the random field model. Different choices of the energy function defining the interaction model are investigated experimentally using a set of 40 mammograms digitized at 2 k
Recognition of clustered microcalcifications using a random field model
Medical Imaging 1993;1905:776-786.