Conference Paper on Non-Rigid Registration (NRR) Validation
Date: Wednesday, January 11 @ 03:53:04 GMT
Topic: Papers and References

Our paper to ISBI 2006 has just been accepted and it is available on-line as well: Abstract: E compare two methods for assessing the performance of groupwise non-rigid registration algorithms. One approach, which has been described previously, utilizes a measure of overlap between data labels. Our new approach exploits the fact that, given a set of non-rigidly registered images, a generative statistical appearance model can be constructed. We observe that the quality of the model depends on the quality of the registration, and can be evaluated by comparing synthetic images sampled from the model with the original image set. We derive indices of model specificity and generalisation that can be used to assess model/registration quality. We show that both approaches detect the loss of registration as a set of correctly registered MR images of the brain is progressively perturbed. We compare the sensitivities of the different methods and show that, as well as requiring no ground truth, our new specificity measure provides the most sensitive approach to detecting misregistration.

This article comes from MARS - Models of Appearance, Registration and Segmentation

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