Abstract
The paper presents a non parametric image registration method based on an explicit representation of the warping function. The image registration problem is approached in the Bayesian framework with a prior term given by a Gaussian random field accounting the regularity of a deformable grid. The observation term is accounted with two terms in the energy functional, the first depends on the quality of recontruction of the target image with respect the source image, the second is based on a set of landmarks automatically detected between the images to be registered. The algorithm is completely unsupervised, making it suitable for high throughput biomedical applications such as proteomics and cellular imaging.
| Original language | English |
|---|---|
| Title of host publication | 2010 IEEE International Conference on Imaging Systems and Techniques, IST 2010 - Proceedings |
| Pages | 279-283 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - 18 Oct 2010 |
| Externally published | Yes |
| Event | 2010 IEEE International Conference on Imaging Systems and Techniques, IST 2010 - Thessaloniki, Greece Duration: 1 Jul 2010 → 2 Jul 2010 |
Other
| Other | 2010 IEEE International Conference on Imaging Systems and Techniques, IST 2010 |
|---|---|
| Country | Greece |
| City | Thessaloniki |
| Period | 1/7/10 → 2/7/10 |
Fingerprint
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Radiology Nuclear Medicine and imaging
Cite this
Bayesian grid matching for 2D gel registration. / Ceccarelli, Michele; Carstensen, Jens Michael.
2010 IEEE International Conference on Imaging Systems and Techniques, IST 2010 - Proceedings. 2010. p. 279-283 5548506.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Bayesian grid matching for 2D gel registration
AU - Ceccarelli, Michele
AU - Carstensen, Jens Michael
PY - 2010/10/18
Y1 - 2010/10/18
N2 - The paper presents a non parametric image registration method based on an explicit representation of the warping function. The image registration problem is approached in the Bayesian framework with a prior term given by a Gaussian random field accounting the regularity of a deformable grid. The observation term is accounted with two terms in the energy functional, the first depends on the quality of recontruction of the target image with respect the source image, the second is based on a set of landmarks automatically detected between the images to be registered. The algorithm is completely unsupervised, making it suitable for high throughput biomedical applications such as proteomics and cellular imaging.
AB - The paper presents a non parametric image registration method based on an explicit representation of the warping function. The image registration problem is approached in the Bayesian framework with a prior term given by a Gaussian random field accounting the regularity of a deformable grid. The observation term is accounted with two terms in the energy functional, the first depends on the quality of recontruction of the target image with respect the source image, the second is based on a set of landmarks automatically detected between the images to be registered. The algorithm is completely unsupervised, making it suitable for high throughput biomedical applications such as proteomics and cellular imaging.
UR - http://www.scopus.com/inward/record.url?scp=77957844237&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77957844237&partnerID=8YFLogxK
U2 - 10.1109/IST.2010.5548506
DO - 10.1109/IST.2010.5548506
M3 - Conference contribution
AN - SCOPUS:77957844237
SN - 9781424464944
SP - 279
EP - 283
BT - 2010 IEEE International Conference on Imaging Systems and Techniques, IST 2010 - Proceedings
ER -