Name
Abstract | ||
|---|---|---|
In this paper a nonlinear registration algorithm for diffusion tensor (DT) MR images is proposed. The nonlinear deformation is modeled using a combination of Discrete Cosine Transformation (DCT) basis functions thus reducing the number of parameters that need to be estimated. This approach was demonstrated to be an effective method for scalar image registration via SPM, and we show here how it can be extended to tensor images. The proposed approach employs the full tensor information via a Euclidean distance metric. Tensor reorientation is explicitly determined from the nonlinear deformation model and applied during the optimization process. We evaluate the proposed approach both quantitatively and qualitatively and show that it results in improved performance in terms of trace error and Euclidean distance error when compared to a tensor registration method (DTI-TK). The computational efficiency of the proposed approach is also evaluated and compared. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CVPRW.2011.5981692 | CVPR 2011 WORKSHOPS |
Keywords | Field | DocType |
nonlinear DTI registration,diffusion tensor MR imaging,DCT basis functions,discrete cosine transformation,nonlinear deformation model,scalar image registration,Euclidean distance metric,tensor reorientation,optimization process,tensor registration method | Nonlinear system,Tensor,Computer science,Scalar (physics),Discrete cosine transform,Artificial intelligence,Basis function,Deformation (mechanics),Topology,Pattern recognition,Euclidean distance,Algorithm,Image registration | Conference |
Volume | Issue | ISSN |
2011 | 1 | 2160-7508 |
ISBN | Citations | PageRank |
978-1-4577-0529-8 | 0 | 0.34 |
References | Authors | |
7 | 2 | |