The wide dynamic range present in digitized mammographic data, partially resulting from the non-uniform thickness of tissue during breast compression, makes it difficult to find window and level values that are appropriate to display the entire image. Further, this factor combined with the non- linearity of the relationship between density and log exposure, confound attempts to automatically derive tissue composition information directly from uncorrected data. This project attempts to address these issues by making appropriate local image corrections based on the characteristic curves of film and digitizer, as well as on the variations in tissue thickness during breast compression. Subjective comparisons of the display techniques developed in this project, to mammography displays based on local histogram equalization methods to reduce image dynamic range, clearly demonstrate superior performance of the methods presented in this paper. In addition to this subjective observation about image display, we also investigated the possibility of using corrected data to improve the performance of tissue composition measurements. A neural network classifier was developed to use features derived from the volume-corrected histogram of the corrected mammographic data to estimate tissue composition. Results indicate that tissue composition measurements are more highly correlated to radiologists' estimates, when they are derived from corrected images.