Abstract
Dynamic enhancement causes serious problems for registration of contrast enhanced breast MRI, due to variable uptakes of agent on different tissues or even same tissues in the breast. We present an iterative optimization algorithm to de-enhance the dynamic contrastenhanced breast MRI and then register them for avoiding the effects of enhancement on image registration. In particular, the spatially varying enhancements are modeled by a Markov Random Field, and estimated by a locally smooth function with boundaries using a graph cut algorithm. The de-enhanced images are then registered by conventional Bspline based registration algorithm. These two steps benefit from each other and are repeated until the results converge. Experimental results show that our two-step registration algorithm performs much better than conventional mutual information based registration algorithm. Also, the effects of tumor shrinking in the conventional registration algorithms can be effectively avoided by our registration algorithm.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 933-941 |
| Number of pages | 9 |
| Volume | 4791 LNCS |
| Edition | PART 1 |
| Publication status | Published - 2007 Dec 1 |
| Externally published | Yes |
| Event | 10th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2007 - Brisbane, Australia Duration: 2007 Oct 29 → 2007 Nov 2 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART 1 |
| Volume | 4791 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 10th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2007 |
|---|---|
| Country | Australia |
| City | Brisbane |
| Period | 07/10/29 → 07/11/2 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Theoretical Computer Science
Cite this
De-enhancing the dynamic contrast-enhanced breast MRI for robust registration. / Zheng, Yuanjie; Yu, Jingyi; Kambhamettu, Chandra; Englander, Sarah; Schnall, Mitchell D.; Shen, Dinggang.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4791 LNCS PART 1. ed. 2007. p. 933-941 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4791 LNCS, No. PART 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - De-enhancing the dynamic contrast-enhanced breast MRI for robust registration
AU - Zheng, Yuanjie
AU - Yu, Jingyi
AU - Kambhamettu, Chandra
AU - Englander, Sarah
AU - Schnall, Mitchell D.
AU - Shen, Dinggang
PY - 2007/12/1
Y1 - 2007/12/1
N2 - Dynamic enhancement causes serious problems for registration of contrast enhanced breast MRI, due to variable uptakes of agent on different tissues or even same tissues in the breast. We present an iterative optimization algorithm to de-enhance the dynamic contrastenhanced breast MRI and then register them for avoiding the effects of enhancement on image registration. In particular, the spatially varying enhancements are modeled by a Markov Random Field, and estimated by a locally smooth function with boundaries using a graph cut algorithm. The de-enhanced images are then registered by conventional Bspline based registration algorithm. These two steps benefit from each other and are repeated until the results converge. Experimental results show that our two-step registration algorithm performs much better than conventional mutual information based registration algorithm. Also, the effects of tumor shrinking in the conventional registration algorithms can be effectively avoided by our registration algorithm.
AB - Dynamic enhancement causes serious problems for registration of contrast enhanced breast MRI, due to variable uptakes of agent on different tissues or even same tissues in the breast. We present an iterative optimization algorithm to de-enhance the dynamic contrastenhanced breast MRI and then register them for avoiding the effects of enhancement on image registration. In particular, the spatially varying enhancements are modeled by a Markov Random Field, and estimated by a locally smooth function with boundaries using a graph cut algorithm. The de-enhanced images are then registered by conventional Bspline based registration algorithm. These two steps benefit from each other and are repeated until the results converge. Experimental results show that our two-step registration algorithm performs much better than conventional mutual information based registration algorithm. Also, the effects of tumor shrinking in the conventional registration algorithms can be effectively avoided by our registration algorithm.
UR - http://www.scopus.com/inward/record.url?scp=38149024848&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38149024848&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9783540757566
VL - 4791 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 933
EP - 941
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -