| bibtype |
J -
Journal Article
|
| ARLID |
0431090 |
| utime |
20240103204542.4 |
| mtime |
20140912235959.9 |
| WOS |
000346975900024 |
| SCOPUS |
84937560793 |
| DOI |
10.1109/TMI.2014.2352791 |
| title
(primary) (eng) |
Bayesian Blind Separation and Deconvolution of Dynamic Image Sequences Using Sparsity Priors |
| specification |
| page_count |
9 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0253240 |
| ISSN |
0278-0062 |
| title
|
IEEE Transactions on Medical Imaging |
| volume_id |
34 |
| volume |
1 (2015) |
| page_num |
258-266 |
|
| keyword |
Functional imaging |
| keyword |
Blind source separation |
| keyword |
Computer-aided detection and diagnosis |
| keyword |
Probabilistic and statistical methods |
| author
(primary) |
| ARLID |
cav_un_auth*0267768 |
| name1 |
Tichý |
| name2 |
Ondřej |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101207 |
| name1 |
Šmídl |
| name2 |
Václav |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA13-29225S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0292734 |
|
| abstract
(eng) |
A common problem of imaging three-dimensional objects into image plane is superposition of the projected structures. In dynamic imaging, projection overlaps of organs and tissues complicate extraction of signals specific to individual structures with different dynamics. The problem manifests itself also in dynamic tomography as tissue mixtures are present in voxels. Separation of signals specific to dynamic structures belongs to the category of blind source separation. It is an underdetermined problem with many possible solutions. Existing separation methods select the solution that best matches their additional assumptions on the source model. We propose a novel blind source separation method based on probabilistic model of dynamic image sequences assuming each source dynamics as convolution of an input function and a source specific kernel (modeling organ impulse response or retention function). These assumptions are formalized as a Bayesian model with hierarchical prior and solved by the Variational Bayes method. |
| reportyear |
2016 |
| RIV |
BB |
| num_of_auth |
2 |
| mrcbC52 |
4 A 4a 20231122140403.0 |
| permalink |
http://hdl.handle.net/11104/0236067 |
| confidential |
S |
| mrcbT16-e |
COMPUTERSCIENCEINTERDISCIPLINARYAPPLICATIONS|ENGINEERINGBIOMEDICAL|ENGINEERINGELECTRICALELECTRONIC|IMAGINGSCIENCEPHOTOGRAPHICTECHNOLOGY|RADIOLOGYNUCLEARMEDICINEMEDICALIMAGING |
| mrcbT16-j |
1.779 |
| mrcbT16-s |
1.900 |
| mrcbT16-4 |
Q1 |
| mrcbT16-B |
93.304 |
| mrcbT16-C |
90.411 |
| mrcbT16-D |
Q1* |
| mrcbT16-E |
Q1* |
| arlyear |
2015 |
| mrcbTft |
\nSoubory v repozitáři: tichy-0431090.pdf |
| mrcbU14 |
84937560793 SCOPUS |
| mrcbU34 |
000346975900024 WOS |
| mrcbU63 |
cav_un_epca*0253240 IEEE Transactions on Medical Imaging 0278-0062 1558-254X Roč. 34 č. 1 2015 258 266 |
|