Department of Pattern Recognition

(eng): We present a novel method for enhancing texture irregularities, both lesions and microcalcifications, in digital X-ray mammograms. It can be implemented in computer-aided diagnostic systems to help improve radiologists’ diagnosis precision. The method provides three different outputs aimed at enhancing three different sizes of mammogram abnormalities. Our approach uses a two-dimensional adaptive causal autoregressive texture model to represent local texture characteristics. Based on these, we enhance suspicious breast tissue abnormalities, such as microcalcifications and masses, to make signs of developing cancer better visually discernible. We extract over 200 local textural features from different frequency bands, which are then combined into a single multichannel image using the Karhunen–Loeve transform. We propose an extension to existing contrast measures for the evaluation of contrast around regions of interest. Our method was extensively tested on the INbreast database and compared both visually and numerically with three state-of-the-art enhancement methods, with favorable results.