0505448 20240103222124.520190612235959.9 85064633416 000469483000017 10.1007/s00138-019-01028-6 10 s. P cav_un_epca*02542180932-8092304 (2019)785-794Springer Mammograms Region of interest enhancement Computer-aided diagnosis Texture model Markov random field 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. 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. http://library.utia.cas.cz/separaty/2019/RO/haindl-0505448.pdf https://link.springer.com/article/10.1007%2Fs00138-019-01028-6 cav_un_auth*0376011 GA19-12340S GA ČR CZ We present a novel method for enhancing texture irregularities, both lesions and microcalcifications, in digital X-ray mammograms. It can be implemented in computer-aided diagnostic systems to help improve radiologists’ diagnosis precision. The method provides three different outputs aimed at enhancing three different sizes of mammogram abnormalities. Our approach uses a two-dimensional adaptive causal autoregressive texture model to represent local texture characteristics. Based on these, we enhance suspicious breast tissue abnormalities, such as microcalcifications and masses, to make signs of developing cancer better visually discernible. We extract over 200 local textural features from different frequency bands, which are then combined into a single multichannel image using the Karhunen–Loeve transform. We propose an extension to existing contrast measures for the evaluation of contrast around regions of interest. Our method was extensively tested on the INbreast database and compared both visually and numerically with three state-of-the-art enhancement methods, with favorable results. WOS BD 20000 20200 20205 2020 2 4 A hod sml 4ah 4as 20231122144046.1 RVO:67985556 http://hdl.handle.net/11104/0297004 1 Department of Pattern Recognition UTIA-B 10201 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE S Copyright 20190408 Copyright Transfer Statement 6 3+4 Article Biochemistry Molecular Biology|Biophysics C ENGINEERING.ELECTRICAL&ELECTRONIC|COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE|COMPUTERSCIENCE.CYBERNETICS 2.000 0.337 6.5 0.00289 0.521 2029 81 0.517 43.42 2.17 637 Q2 1.474 86 Q3 35.038 36.5 Q3 Q4 0.46 Q3 38.636 2019 Soubory v repozitáři: haindl-0505448.pdf, haindl-0505448-Copyright.pdf 85064633416 SCOPUS PUBMED 000469483000017 WOS cav_un_epca*0254218 Machine Vision and Applications 0932-8092 1432-1769 Roč. 30 č. 4 2019 785 794 Springer