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* Corresponding author 1 EPIDAURE - Medical imaging and robotics CRISAM - Inria Sophia Antipolis - Méditerranée

Abstract : Registration of 3D medical images consists in computing the -best- transformation between two acquisitions, or equivalently, determines the point to point correspondence between the images. Registration algorithms are usually based either on features extracted from the image feature-based approaches or on the optimization of a similarity measure of the images intensities intensitybased or iconic approaches. Another classification criterion is the type of transformation sought e.g. rigid or non-rigid. In this chapter, we concentrate on feature-based approaches for rigid registration, similar approaches for non-rigid registration being reported in another set of publication 35, 36. We show how to reduce the dimension of the registration problem by first extracting a surface from the 3D image, then landmark curves on this surface and possibly landmark points on these curves. This concept proved its efficiency through many applications in medical image analysis as we will see in the sequel. This work has been for a long time a central investigation topic of the Epidaure team 2 and we can only reflect here on a small part of the research done in this area. We present in the first section the notions of crest lines and extremal points and how these differential geometry features can be extracted from 3D images. In Section 2, we focus on the different rigid registration algorithms that we used to register such features. The last section analyzes the possible errors in this registration scheme and demonstrates that a very accurate registration could be achieved.

Keywords : registration matching validation uncertainty

Autor: Xavier Pennec - Nicholas Ayache - Jean-Philippe Thirion -



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