0382295 20240103201411.820121105235959.9 11 s. P cav_un_epca*0382294978-80-01-05130-651-61PrahaCzech Technical University in Prague2012 Screening mammography Texture information Local statistical model Log-likelihood image Structural Gaussian mixture cav_un_auth*0101091 Grim Jiří 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*0285165 Lee G. L. AU http://library.utia.cas.cz/separaty/2012/RO/grim-evaluation of screening mammograms by local structural mixture modelsr.pdf GAP403/12/1557 GA ČR CZ cav_un_auth*0308953 GA102/08/0593 GA ČR cav_un_auth*0239567 We consider the recently proposed evaluation of screening mammograms by local statistical models. The model is defined as a joint probability density of inside grey levels of a suitably chosen search window. We approximate the model density by a mixture of Gaussian densities. Having estimated the mixture parameters we calculate at all window positions the corresponding log-likelihood values which can be displayed as grey levels at the respective window centers. The resulting log-likelihood image closely correlates with the original mammogram and emphasizes the structural details. In this paper we try to enhance the log-likelihood images by using structural mixture model capable of suppressing the influence of noisy variables. cav_un_auth*0284707 Stochastic and Physical Monitoring Systems Zlenice near Prague 25.06.2012-30.06.2012 CZ 2013 IN 2 ZP RVO:67985556 http://hdl.handle.net/11104/0212556 2012 cav_un_epca*0382294 Stochastic and Physical Monitoring Systems SPSM 2012 978-80-01-05130-6 51 61 Praha Czech Technical University in Prague 2012