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Assessing the Accuracy of Non-Rigid Registration With and Without Ground Truth

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Abstract:

We present two methods for assessing the performance of non-rigid registration algorithms. We also show that assessment can be carried out with or without the need for some form of ground truth. One method utilizes a measure of overlap among data labels. The other method exploits the fact that, given a set of non-rigidly registered images, a generative statistical appearance model can be constructed. The quality of the model depends on the quality of the registration, and can be evaluated by comparing images sampled from it with the original image set. We derive indices of model specificity and generalisation, as well as introduce a formulation for overlap among anatomical labels. We show that all of them demonstrate the loss of registration as a set of correctly registered images is progressively perturbed. Finally, we compare the sensitivities of these methods.