0454449 20240103211644.620160215235959.9 84962799507 000380431200025 10.1109/IWCIM.2015.7347085 5 s. E cav_un_epca*0454448978-1-4673-8457-51-5New York, NY, IEEE2015 Breast cancer breast density Mammography MRF ACR BI-RADS cav_un_auth*0286710 Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition Remeš Václav UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101093 Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition Haindl Michal UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2016/RO/haindl-0454449.pdf cav_un_auth*0303439 GA14-10911S GA ČR CZ Breast density is an important cue to detect both the presence of suspicious cancerous masses and to predict future possibility for cancer development. A fast breast density classification method is presented and successfully tested on two state-of-the-art mammogram databases. The X-ray digital mammogram tissue texture is locally represented by the two-dimensional adaptive causal autoregressive spatial model and its parameters are used as the classification features. cav_un_auth*0325650 IWCIM 2015 International Workshop on Computational Intelligence for Multimedia Understanding 29.10.2015-30.10.2015 Praha CZ BD 2016 PR RVO:67985556 http://hdl.handle.net/11104/0257087 S 2015 84962799507 SCOPUS 000380431200025 WOS cav_un_epca*0454448 2015 International Workshop on Computational Intelligence for Multimedia Understanding 978-1-4673-8457-5 1 5 New York, NY, IEEE 2015