0445250 20240103210234.320150723235959.9 10.5772/60988 16 s. P 120 cav_un_epca*0445249978-953-51-2138-163-78ZagrebInTech Education and Publishing2015FernandesFabiano CavalcantiBrasilLourdes Mattosda Veiga GuadagninRenato mammogram enhancement Markov random field texture model 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/2015/RO/haindl-0445250.pdf GA14-10911S GA ČR CZ cav_un_auth*0303439 Three fully automatic methods for X-ray digital mammogram enhancement based on a fast analytical textural model are presented. These efficient single and double view enhancement methods are based on the underlying two-dimensional adaptive causal autoregressive texture model. The~methods locally predict breast tissue texture from single or double view mammograms and enhance breast tissue abnormalities, such as the sign of a developing cancer, using the estimated model prediction statistics. The~double-view mammogram enhancement is based on the cross-prediction of two mutually registered left and right breasts' mammograms or alternatively a temporal sequence of mammograms. The single-view mammogram enhancement is based on modeling prediction error in case of not the both breasts' mammograms being available. 2016 BD 2 RVO:67985556 http://hdl.handle.net/11104/0247980 S 2015 cav_un_epca*0445249 Mammography Techniques and Review 978-953-51-2138-1 63 78 Zagreb InTech Education and Publishing 2015 Fernandes Fabiano Cavalcanti 340 Brasil Lourdes Mattos 340 da Veiga Guadagnin Renato 340