bibtype J - Journal Article
ARLID 0322909
utime 20240103191500.1
mtime 20090422235959.9
WOS 000264397100007
DOI 10.1109/TIP.2008.2011168
title (primary) (eng) Computer-Aided Evaluation of Screening Mammograms Based on Local Texture Models
specification
page_count 9 s.
serial
ARLID cav_un_epca*0253235
ISSN 1057-7149
title IEEE Transactions on Image Processing
volume_id 18
volume 4 (2009)
page_num 765-773
publisher
name Institute of Electrical and Electronics Engineers
title (cze) Počítačové vyhodnocování mamogramů pomocí lokálního modelu textury
keyword Screening mammography
keyword texture information
keyword local statistical model
keyword Gaussian mixture
author (primary)
ARLID cav_un_auth*0101091
name1 Grim
name2 Jiří
institution UTIA-B
full_dept Department of Pattern Recognition
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101197
name1 Somol
name2 Petr
institution UTIA-B
full_dept Department of Pattern Recognition
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101093
name1 Haindl
name2 Michal
institution UTIA-B
full_dept Department of Pattern Recognition
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0050523
name1 Daneš
name2 J.
country CZ
cas_special
project
project_id GA102/07/1594
agency GA ČR
ARLID cav_un_auth*0228611
project
project_id GA102/08/0593
agency GA ČR
ARLID cav_un_auth*0239567
project
project_id 1M0572
agency GA MŠk
ARLID cav_un_auth*0001814
project
project_id 2C06019
agency GA MŠk
country CZ
ARLID cav_un_auth*0216518
research CEZ:AV0Z10750506
abstract (eng) We propose a new approach to diagnostic evaluation of screening mammograms based on local statistical texture models. The local evaluation tool has the form of a multivariate probability density of gray levels in a suitably chosen search window. First the density function in the form of Gaussian mixture is estimated from data obtained by scanning of the mammogram with the search window. Then we evaluate the estimated mixture at each position and display the corresponding log-likelihood value as a gray level at the window center. The resulting log-likelihood image closely correlates with the structural details of the original mammogram and emphasizes unusual places. We assume that, in parallel use, the log-likelihood image may provide additional information to facilitate the identification of malignant lesions as untypical locations of high novelty.
abstract (cze) Předmětem článku je návrh nové metody vyhodnocování mamomagramů pomocí lokálního statistického modelu. Výsledkem zpracování mamogramu je věrohodnostní transformace původního mamogramu, na které jsou zvýrazněna neobvyklá místa se zvýšenou pravděpodobností výskytu patologických změn.
reportyear 2009
RIV BB
permalink http://hdl.handle.net/11104/0171042
mrcbT16-f 4.139
mrcbT16-g 0.248
mrcbT16-h 7.6
mrcbT16-i 0.03222
mrcbT16-j 1.408
mrcbT16-k 11472
mrcbT16-l 214
mrcbT16-q 156
mrcbT16-s 3.178
mrcbT16-y 33.51
mrcbT16-x 4.81
arlyear 2009
mrcbU34 000264397100007 WOS
mrcbU63 cav_un_epca*0253235 IEEE Transactions on Image Processing 1057-7149 1941-0042 Roč. 18 č. 4 2009 765 773 Institute of Electrical and Electronics Engineers