Abstract
In earlier work [1], we demonstrated that cortical registration could be improved on simulated data by using blurred, geometric, imagebased features (L vv ) or explicitly extracted and blurred sulcal traces on simulated data. Here, the technique is modified to incorporate sulcal ribbons in conjunction with a chamfer distance objective function to improve registration in real MRI data as well. Experiments with 10 simulated data sets demonstrate a 56% reduction in residual sulcal registration error (from 3.4 to 1.5mm, on average) when compared to automatic linear registration and an 28% improvement over our previously published non-linear technique (from 2.1 to 1.5mm). The simulation results are confirmed by experiments with real MRI data from young normal subjects, where sulcal misregistration is reduced by 20% (from 5.0mm to 4.0mm) and 11% (from 4.5 to 4.0mm) over the standard linear and nonlinear registration methods, respectively.
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Keywords
- Anatomical Variability
- Standard Animal
- Computer Assist Tomography
- Nonlinear Registration
- Stereotaxic Space
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Collins, D.L., Le Goualher, G., Evans, A.C. (1998). Non-linear cerebral registration with sulcal constraints. In: Wells, W.M., Colchester, A., Delp, S. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI’98. MICCAI 1998. Lecture Notes in Computer Science, vol 1496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056286
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