Difference between revisions of "2011 Winter Project Week:RegistrationAnisotropy"
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Aside from designing experiments for metrics that are specific to this effect, we also seek interaction with other projects that have highly anisotropic image data. | Aside from designing experiments for metrics that are specific to this effect, we also seek interaction with other projects that have highly anisotropic image data. | ||
| + | Example Experiment: produce directly joint histograms and difference(ratio) images w/o the need to run the actual registration, i.e. we compare the effects of voxel anisotropy on the theoretical optimum: Move, filter and subsample identical image pair, then resample back to original position and build joint histograms and subtraction images. Because of the increasing PV effects we expect to see a degenerating joint histogram and a subtraction image with increasing edge artifacts. We can then try to interpret how the optimizer will behave in this environment. The benefit of this metric is that we circumvent the stochastic nature of the registration output. | ||
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== Progress == | == Progress == | ||
project links go here | project links go here | ||
Revision as of 20:28, 9 December 2010
Home < 2011 Winter Project Week:RegistrationAnisotropy
The 3DSlicer Registration Case Library Project
Key Investigators
- BWH: Dominik Meier, Andryi Fedorov
Objective
For this work we seek insight into the effects of voxel anisotropy and image inhomogeneity on registration accuracy and robustness.
Approach, Plan
Aside from designing experiments for metrics that are specific to this effect, we also seek interaction with other projects that have highly anisotropic image data.
Example Experiment: produce directly joint histograms and difference(ratio) images w/o the need to run the actual registration, i.e. we compare the effects of voxel anisotropy on the theoretical optimum: Move, filter and subsample identical image pair, then resample back to original position and build joint histograms and subtraction images. Because of the increasing PV effects we expect to see a degenerating joint histogram and a subtraction image with increasing edge artifacts. We can then try to interpret how the optimizer will behave in this environment. The benefit of this metric is that we circumvent the stochastic nature of the registration output.
Progress
Progress
project links go here
