We formulate the problem of microcalcification detection in digital mammograms as a statistical change detection problem in the local properties of the image. First, we represent mammograms by two-dimensional autoregressive moving-average (2D ARMA) fields; thus uniquely characterizing the images by their reduced dimensionality 2D ARMA feature vectors. Texture changes in mammograms are then modeled as an additive change in the mean parameter of the PDF associated with the 2D ARMA feature vector sequence that describes the image. A generalized likelihood ratio test is used to detect theses changes and estimate the exact time (or space) where they occur. Our simulation results on the Digital Database for Screening Mammography hosted by the University of South Florida show that the decision functions of cancerous images present high peaks at the microcalcification locations, whereas they exhibit a uniform behavior for healthy mammograms. The proposed algorithm achieves a sensitivity and specificity of 96.9% and 97.8%, respectively.