| bibtype |
M -
Monography Chapter
|
| ARLID |
0445250 |
| utime |
20240103210234.3 |
| mtime |
20150723235959.9 |
| DOI |
10.5772/60988 |
| title
(primary) (eng) |
Digital Mammogram Enhancement |
| specification |
| page_count |
16 s. |
| media_type |
P |
| book_pages |
120 |
|
| serial |
| ARLID |
cav_un_epca*0445249 |
| ISBN |
978-953-51-2138-1 |
| title
|
Mammography Techniques and Review |
| page_num |
63-78 |
| publisher |
| place |
Zagreb |
| name |
InTech Education and Publishing |
| year |
2015 |
|
| editor |
| name1 |
Fernandes |
| name2 |
Fabiano Cavalcanti |
|
| editor |
| name1 |
Brasil |
| name2 |
Lourdes Mattos |
|
| editor |
| name1 |
da Veiga Guadagnin |
| name2 |
Renato |
|
|
| keyword |
mammogram enhancement |
| keyword |
Markov random field |
| keyword |
texture model |
| author
(primary) |
| ARLID |
cav_un_auth*0101093 |
| name1 |
Haindl |
| name2 |
Michal |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept (eng) |
Department of Pattern Recognition |
| department (cz) |
RO |
| department (eng) |
RO |
| 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*0286710 |
| name1 |
Remeš |
| name2 |
Václav |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept |
Department of Pattern Recognition |
| department (cz) |
RO |
| department |
RO |
| 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 |
GA14-10911S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0303439 |
|
| abstract
(eng) |
Three 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 being available. |
| reportyear |
2016 |
| RIV |
BD |
| num_of_auth |
2 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0247980 |
| confidential |
S |
| arlyear |
2015 |
| mrcbU63 |
cav_un_epca*0445249 Mammography Techniques and Review 978-953-51-2138-1 63 78 Zagreb InTech Education and Publishing 2015 |
| mrcbU67 |
Fernandes Fabiano Cavalcanti 340 |
| mrcbU67 |
Brasil Lourdes Mattos 340 |
| mrcbU67 |
da Veiga Guadagnin Renato 340 |
|