Name
Abstract | ||
|---|---|---|
Intensity value-based registration is a widely used technique for the spatial alignment of medical images. Generally, the registration transformation is determined by iteratively optimizing a similarity measure calculated from the grey values of both images. However, such algorithms may have high computational costs, especially in the case of multi-modality registration, which makes their integration into systems difficult. At present, registration based on mutual information (MI) still requires computation times of the order of several minutes. In this contribution we focus on a new similarity measure based on local correlation (LC) which is, well-salted for numerical optimization. We show that LC can be formulated as a least-squares criterion which allows the use of dedicated methods. Thus, it is possible to register MR neuro perfusion time-series (128(2) x 30 voxel, 40 images) on a moderate workstation in real-time: the registration of an image takes about 500 ms and is therefore several times faster than image acquisition time. For the registration of CT-MR images (512(2) x 87 CT, 256(2) x 128 MR) a multiresolution framework is used. On top of the decomposition, which requires 47 s of computation time, the optimization with an algorithm based on MI previously described in the literature takes 97 s. In contrast; the proposed approach only takes 13 s, corresponding to a speedup, about a factor of 7. Furthermore, we demonstrate that the superior computational performance of LC is not gained at the expense of accuracy. In particular, experiments with dual contrast MR images providing ground truth for the registration show a comparable sub-voxel accuracy of LC and MI similarity. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/ICCV.2001.937595 | ICCV |
Keywords | Field | DocType |
image registration,least squares approximations,medical image processing,optimisation,real-time systems,CT-MR images,MR neuro perfusion time-series,computational costs,intensity value-based registration,least-squares criterion,local correlation,multiresolution framework,numerical optimization,real-time multi-modality 3-D medical image registration,registration transformation,similarity measure,spatial alignment | Voxel,Computer vision,Similarity measure,Pattern recognition,Medical imaging,Computer science,Robustness (computer science),Mutual information,Artificial intelligence,Image resolution,Image registration,Speedup | Conference |
Volume | Citations | PageRank |
1 | 14 | 1.95 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Netsch | 1 | 93 | 16.47 |
| Peter Rösch | 2 | 90 | 14.36 |
| Arianne Van Muiswinkel | 3 | 40 | 6.88 |
| Jürgen Weese | 4 | 774 | 92.69 |