| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0317588 |
| utime |
20240111140712.6 |
| mtime |
20081218235959.9 |
| title
(primary) (eng) |
Unsupervised Mammograms Segmentation |
| specification |
| page_count |
4 s. |
| media_type |
www |
|
| serial |
| ARLID |
cav_un_epca*0317587 |
| ISBN |
978-1-4244-2174-9 |
| title
|
Proceedings of the 19th International Conference on Pattern Recognition |
| page_num |
676-679 |
| publisher |
| place |
Los Alamitos |
| name |
IEEE Press |
| year |
2008 |
|
|
| title
(cze) |
Neřízená segmentace mamogramů |
| keyword |
mammography |
| keyword |
cancer detection |
| keyword |
image unsupervised segmentation |
| keyword |
Markov random fields |
| author
(primary) |
| 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*0101165 |
| name1 |
Mikeš |
| name2 |
Stanislav |
| institution |
UTIA-B |
| full_dept |
Department of Pattern Recognition |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
507752 |
| country |
XE |
| agency |
EC |
| ARLID |
cav_un_auth*0200689 |
|
| project |
| project_id |
1ET400750407 |
| agency |
GA AV ČR |
| ARLID |
cav_un_auth*0001797 |
|
| 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 |
|
| project |
| project_id |
IAA2075302 |
| agency |
GA AV ČR |
| ARLID |
cav_un_auth*0001801 |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
We present a multiscale unsupervised segmenter for automatic detection of potentially cancerous regions of interest containing fibroglandular tissue in digital screening mammography. The mammogram tissue textures are locally represented by four causal multispectral random field models recursively evaluated for each pixel and several scales. The segmentation part of the algorithm is based on the underlying Gaussian mixture model and starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous mammogram segments is reached. The performance of the presented method is verified on the Digital Database for Screening Mammography (DDSM) from the University of South Florida as well as extensively tested on the Prague Texture Segmentation Benchmark and compares favourably with several alternative unsupervised texture segmentation methods. |
| abstract
(cze) |
Článek prezentuje víceměřítkovou neřízenou metodu automatické segmentace pro rozpoznávání potenciálně rakovinných oblastí zájmu, které obsahují fibrozně-žlázovitou tkáň, z digitálních roentgenových mamogramů. Mamografické tkáňové textury jsou lokálně reprezentovány čtyřmi kauzálními modely náhodných polí rekurzivně odhadovanými pro každý pixel. Segmentační část metody je založená na gaussovském směsovém modelu. Segmentace začíná z přesegmentovaného odhadu, který se adaptivně mění, až se dosáhne optimální počet homogenních oblastí mamogramu. Vlastnosti publikované metody jsou rozsáhle ověřovány na Digital Database for Screening Mammography (DDSM) z University of South Florida a na Prague Texture Segmentation Benchmark pomocí nejpoužívanějších segmentačních kriterií. Metoda dosahuje lepší výsledky než několik alternativních testovaných texturních segmentačních metod. |
| action |
| ARLID |
cav_un_auth*0245453 |
| name |
19th International Conference on Pattern Recognition |
| place |
Tampa |
| dates |
07.12.2008-11.12.2008 |
| country |
US |
|
| reportyear |
2010 |
| RIV |
BD |
| permalink |
http://hdl.handle.net/11104/0167195 |
| arlyear |
2008 |
| mrcbU56 |
pdf |
| mrcbU63 |
cav_un_epca*0317587 Proceedings of the 19th International Conference on Pattern Recognition 978-1-4244-2174-9 676 679 Los Alamitos IEEE Press 2008 |
|