Efficient textural model-based mammogram enhancement

Abstract

An efficient method for X-ray digital mammogram multi-view enhancement based on the underlying two-dimensional adaptive causal autoregressive texture model is presented. The method locally predicts breast tissue texture from multi-view mammograms and enhances breast tissue abnormalities, such as the sign of a developing cancer, using the estimated model prediction error. The mammo-gram enhancement is based on the cross-prediction error of mutually registered left and right breasts mammograms or on the single-view model prediction error if both breasts' mammograms are not available.