The presentation of images with lesions of known pathology that are similar to an unknown lesion may be helpful to radiologists in the diagnosis of challenging cases for improving the diagnostic accuracy and also for reducing variation among different radiologists. The authors have been developing a computerized scheme for automatically selecting similar images with clustered microcalcifications on mammograms from a large database. For similar images to be useful, they must be similar from the point of view of the diagnosing radiologists. In order to select such images, subjective similarity ratings were obtained for a number of pairs of clustered microcalcifications by breast radiologists for establishment of a "gold standard" of image similarity, and the gold standard was employed for determination and evaluation of the selection of similar images. The images used in this study were obtained from the Digital Database for Screening Mammography developed by the University of South Florida. The subjective similarity ratings for 300 pairs of images with clustered microcalcifications were determined by ten breast radiologists. The authors determined a number of image features which represent the characteristics of clustered microcalcifications that radiologists would use in their diagnosis. For determination of objective similarity measures, an artificial neural network (ANN) was employed. The ANN was trained with the average subjective similarity ratings as teacher and selected image features as input data. The ANN was trained to learn the relationship between the image features and the radiologists' similarity ratings; therefore, once the training was completed, the ANN was able to determine the similarity, called a psychophysical similarity measure, which was expected to be close to radiologists' impressions, for an unknown pair of clustered microcalcifications. By use of a leave-one-out test method, the best combination of features was selected. The correlation coefficient between the gold standard and the psychophysical similarity measure through the use of seven features was relatively high (r=0.71) and was comparable to the correlation coefficients between the ratings by one radiologist and the average ratings by nine radiologists (r=0.69 +/- 0.07). The correlation coefficient was improved compared to that of a distance-based method (r=0.58). The result indicated that similar images selected by the psychophysical similarity measure may be useful to radiologists in the diagnosis of clustered microcalcifications on mammograms.