The Use of Robust Local Hausdorff Distances in Accuracy Assessment for Image Alignment of Brain MRI
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/1926/1354
We present and implement an error estimation protocol in the Insight Toolkit (ITK) for assessing the
accuracy of image alignment. We base this error estimation on a robust version of the HausdorffDistance
(HD) metric applied to the recovered edges of the images. The robust modifications we introduce to
the HD metric significantly reduce the amount of outliers in the local distance error estimation. We
evaluate the accuracy of our protocol on synthetically deformed images. We provide the source code
and datasets to reproduce this evaluation. The proposed method is shown to improve error assessment
when it is compared with conventional HD methods. This approach has many applications including
local estimation and visual assessment of registration error and registration parameter selection.
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Comment by Amina Kharbach yellow
priere d'inserer le code utiliser pour mettre en place cet algo


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Keywords: Accuracy assessment, local Hausdorff
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