Evaluating 3D registration of CT-scan images using crest lines

We consider the issue of matching 3D objects extracted from medical images. We show that crest lines computed on the object surfaces correspond to meaningful anatomical features, and that they are stable with respect to rigid transformations. We present the current chain of algorithmic modules which automatically extract the major crest lines in 3D CT-Scan images, and then use differential invariants on these lines to register together the 3D images with a high precision. The extraction of the crest lines is done by computing up to third order derivatives of the image intensity function with appropriate 3D filtering of the volumetric images, and by the 'marching lines' algorithm. The recovered lines are then approximated by splines curves, to compute at each point a number of differential invariants. Matching is finally performed by a new geometric hashing method. The whole chain is now completely automatic, and provides extremely robust and accurate results, even in the presence of severe occlusions. In this paper, we briefly describe the whole chain of processes, already presented to evaluate the accuracy of the approach on a couple of CT-scan images of a skull containing external markers.