For the detection of microcalcifications in mammograms, accurate noise estimation is of crucial importance. In this paper we present a method that makes a robust estimate of the signal dependent image noise, by taking into account quantum noise and detector inhomogeneity. In digital mammograms, high frequency image noise is dominated by quantum noise, which in raw images can be described by a square root model where the noise is proportional to the pixel value. However, due to detector inhomogeneity, the anode heel effect and other sources of variation, noise properties vary across an image. We developed a method that deals with these effects in a general way, by making a nonuniform noise model that is pixel value and location dependent. This is established by subdividing the image into tiles. In each tile a square root model of the noise is estimated, that by interpolation gives a model of the noise as a function of pixel value and location. Results indicate some improvement in microcalcification detection, when this model is used to estimate noise in images acquired with an inhomogeneous detector.