This paper deals with the problem of texture feature extraction in digital mammograms. Our main goal is to generate texture features that are able to "summarize" meaningful information in the mammogram. Subsequently, we use these features to discriminate between texture representing clusters of microcalcifications and texture representing normal tissue. Having a two-class problem, we suggest a texture feature extraction method based on a single filter optimized with respect to the Fisher criterion. The advantage of this criterion is that it uses both the feature mean and the feature variance to achieve good feature separation. Results from an experimental study indicate that the proposed method is useful for texture feature extraction in digital mammograms.