To discover clustered microcalcifications in computerized mammogram image analysis firstly we must define what we mean by calcifications in digitized mammographic images. Here we show that given an objective function based on the invariance across logGabor filters actively responding to different aspects of obvious and subtle objects in digitized mammograms, the optimal notion of microcalcification can be defined as congruence in local entropy at attentional points across a range of 2D frequency bands. We reach this conclusion from several experiments in which the notion of microcalcification as congruence in statistical structure across frequency bands was applied on a number of digitized mammograms from the MIAS and Nijmegen databases in order to investigate the segregation of obvious and subtle objects.