| bibtype |
J -
Journal Article
|
| ARLID |
0456983 |
| utime |
20240103211945.9 |
| mtime |
20160316235959.9 |
| SCOPUS |
84990911120 |
| WOS |
000385338900009 |
| DOI |
10.1016/j.cviu.2015.11.010 |
| title
(primary) (eng) |
Non-parametric Bayesian models of response function in dynamic image sequences |
| specification |
| page_count |
11 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0252571 |
| ISSN |
1077-3142 |
| title
|
Computer Vision and Image Understanding |
| volume_id |
151 |
| volume |
1 (2016) |
| page_num |
90-100 |
| publisher |
|
|
| keyword |
Response function |
| keyword |
Blind source separation |
| keyword |
Dynamic medical imaging |
| keyword |
Probabilistic models |
| keyword |
Bayesian methods |
| author
(primary) |
| ARLID |
cav_un_auth*0267768 |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| full_dept |
Department of Adaptive Systems |
| name1 |
Tichý |
| name2 |
Ondřej |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101207 |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| name1 |
Šmídl |
| name2 |
Václav |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0292734 |
| project_id |
GA13-29225S |
| agency |
GA ČR |
|
| abstract
(eng) |
Estimation of response functions is an important task in dynamic medical imaging. This task arises for example in dynamic renal scintigraphy, where impulse response or retention functions are estimated, or in functional magnetic resonance imaging where hemodynamic response functions are required. These functions can not be observed directly and their estimation is complicated because the recorded images are subject to superposition of underlying signals. Therefore, the response functions are estimated via blind source separation and deconvolution. Performance of this algorithm heavily depends on the used models of the response functions. Response functions in real image sequences are rather complicated and finding a suitable parametric form is problematic. In this paper, we study estimation of the response functions using non-parametric Bayesian priors. These priors were designed to favor desirable properties of the functions, such as sparsity or smoothness. |
| RIV |
BB |
| reportyear |
2017 |
| num_of_auth |
2 |
| mrcbC52 |
4 A hod 4ah 20231122141546.1 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0258398 |
| mrcbC64 |
1 Department of Adaptive Systems UTIA-B 10201 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE |
| confidential |
S |
| mrcbC86 |
2 Article Computer Science Artificial Intelligence|Engineering Electrical Electronic |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE|ENGINEERINGELECTRICALELECTRONIC |
| mrcbT16-j |
1.069 |
| mrcbT16-s |
1.297 |
| mrcbT16-4 |
Q1 |
| mrcbT16-B |
76.897 |
| mrcbT16-D |
Q1 |
| mrcbT16-E |
Q1 |
| arlyear |
2016 |
| mrcbTft |
\nSoubory v repozitáři: tichy-0456983.pdf |
| mrcbU14 |
84990911120 SCOPUS |
| mrcbU34 |
000385338900009 WOS |
| mrcbU63 |
cav_un_epca*0252571 Computer Vision and Image Understanding 1077-3142 1090-235X Roč. 151 č. 1 2016 90 100 Elsevier |
|