Name
Abstract | ||
|---|---|---|
Multi-modal image registration is a challenging problem in medical imaging. The goal is to align anatomically identical structures; however, their appearance in images acquired with different imaging devices, such as CT or MR, may be very different. Registration algorithms generally deform one image, the floating image, such that it matches with a second, the reference image, by maximizing some similarity score between the deformed and the reference image. Instead of using a universal, but a priori fixed similarity criterion such as mutual information, we propose learning a similarity measure in a discriminative manner such that the reference and correctly deformed floating images receive high similarity scores. To this end, we develop an algorithm derived from max-margin structured output learning, and employ the learned similarity measure within a standard rigid registration algorithm. Compared to other approaches, our method adapts to the specific registration problem at hand and exploits correlations between neighboring pixels in the reference and the floating image. Empirical evaluation on CT-MR/PET-MR rigid registration tasks demonstrates that our approach yields robust performance and outperforms the state of the art methods for multi-modal medical image registration. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CVPR.2009.5206840 | computer vision and pattern recognition |
Keywords | Field | DocType |
standard rigid registration algorithm,a priori fixed similarity criterion,learning (artificial intelligence),floating image,multimodal 3d image registration,imaging devices,multimodal medical image registration,image registration,medical imaging,reference image,medical image processing,max-margin structured output learning method,biology,mutual information,computed tomography,pixel,histograms,biomedical imaging,learning artificial intelligence,cybernetics,3d imaging,maximum likelihood estimation | Computer vision,Pattern recognition,Similarity measure,Medical imaging,Computer science,A priori and a posteriori,Mutual information,Artificial intelligence,Pixel,Discriminative model,Modal,Image registration | Conference |
Volume | Issue | ISSN |
2009 | 1 | 1063-6919 |
ISBN | Citations | PageRank |
978-1-4244-3992-8 | 24 | 1.13 |
References | Authors | |
9 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| donghan lee | 1 | 24 | 1.13 |
| Matthias Hofmann | 2 | 24 | 1.13 |
| Florian Steinke | 3 | 269 | 19.19 |
| yasemin altun | 4 | 2463 | 150.46 |
| Nathan D. Cahill | 5 | 134 | 19.33 |
| Bernhard Schölkopf | 6 | 23120 | 3091.82 |