0397607 20240103203125.120131112235959.9 000341591900101 10.1109/CBMS.2013.6627859 2 s. E cav_un_epca*0397606978-1-4799-1053-3522-523PiscatawayIEEE2013 mammogram enhancement autoregressive texture model breast tissue modeling cav_un_auth*0101093 Haindl Michal Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0286710 Remeš Václav Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2013/RO/haindl-0397607.pdf 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~mammogram 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. cav_un_auth*0295134 2013 IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS) Porto 20.06.2013-22.06.2013 PT 2014 BD 2 PR RVO:67985556 http://hdl.handle.net/11104/0225916 2013 000341591900101 WOS cav_un_epca*0397606 2013 IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS) 978-1-4799-1053-3 522 523 Piscataway IEEE 2013