| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0436549 |
| utime |
20240103205225.4 |
| mtime |
20150119235959.9 |
| SCOPUS |
84928562793 |
| WOS |
000380564200011 |
| DOI |
10.1109/SITIS.2014.53 |
| title
(primary) (eng) |
Adaptive Model-Based Mammogram Enhancement |
| specification |
| page_count |
8 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0436546 |
| ISBN |
978-1-4799-7978-3 |
| title
|
Proceedings of the Tenth International Conference on Signal-Image Technology & Internet-Based Systems, SITIS 2014 |
| page_num |
65-72 |
| publisher |
| place |
Los Alamitos, USA |
| name |
IEEE Computer Society CPS |
| year |
2014 |
|
| editor |
| name1 |
Yetongno |
| name2 |
Kokou |
|
| editor |
| name1 |
Dipanda |
| name2 |
Albert |
|
| editor |
| name1 |
Chbeir |
| name2 |
Richard |
|
|
| keyword |
mammography |
| keyword |
image enhancement |
| keyword |
MRF |
| keyword |
textural models |
| author
(primary) |
| ARLID |
cav_un_auth*0101093 |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept (eng) |
Department of Pattern Recognition |
| department (cz) |
RO |
| department (eng) |
RO |
| full_dept |
Department of Pattern Recognition |
| name1 |
Haindl |
| name2 |
Michal |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0286710 |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept |
Department of Pattern Recognition |
| department (cz) |
RO |
| department |
RO |
| full_dept |
Department of Pattern Recognition |
| name1 |
Remeš |
| name2 |
Václav |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0303439 |
| project_id |
GA14-10911S |
| agency |
GA ČR |
| country |
CZ |
|
| abstract
(eng) |
Five fully automatic methods for X-ray digital mammogram enhancement based on a fast analytical textural model are presented. These efficient single and double view enhancement methods are based on the underlying two-dimensional adaptive causal autoregressive texture model. The~methods locally predict breast tissue texture from single or double view mammograms and enhance breast tissue abnormalities, such as the sign of a developing cancer, using the estimated model prediction statistics. The~double-view mammogram enhancement is based on the cross-prediction of two mutually registered left and right breasts mammograms or alternatively a temporal sequence of mammograms. The single-view mammogram enhancement is based on modeling prediction error in case of not the both breasts' mammograms are available. |
| action |
| ARLID |
cav_un_auth*0310868 |
| name |
Tenth International Conference on Signal-Image Technology & Internet-Based Systems (SITIS 2014) |
| dates |
23.11.2014-27.11.2014 |
| place |
Marrakech |
| country |
MA |
|
| RIV |
BD |
| reportyear |
2015 |
| num_of_auth |
2 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0242828 |
| confidential |
S |
| arlyear |
2014 |
| mrcbU14 |
84928562793 SCOPUS |
| mrcbU34 |
000380564200011 WOS |
| mrcbU63 |
cav_un_epca*0436546 Proceedings of the Tenth International Conference on Signal-Image Technology & Internet-Based Systems, SITIS 2014 978-1-4799-7978-3 65 72 Los Alamitos, USA IEEE Computer Society CPS 2014 |
| mrcbU67 |
Yetongno Kokou 340 |
| mrcbU67 |
Dipanda Albert 340 |
| mrcbU67 |
Chbeir Richard 340 |
|