Conference Paper

Skull Registration Using Rigid Super-Curves

DOI: 10.1109/CGIV.2009.62 Conference: Sixth International Conference on Computer Graphics, Imaging and Visualization: New Advances and Trends, CGIV 2009, 11-14 August 2009, Tianjin, China
Source: DBLP


This research presents an algorithm, Rigid Super Curves (RSC), to solve the problem of registering two sets of digitized skulls data under a rigid transformation using crest lines. The method that restitutes the rigid transformation between two sets of fully matched curves is propounded. RSC exploits the non-ambiguity of B-Spline representation of super-curves whilst overcoming the inability of super-curves to restore rigid transformations. A further contribution of this study is a two-stage algorithm based on RSC which registers two sets of partially matched curves under a rigid transformation. The algorithm improves the robustness over feature based methods by considering the structure rather than individual points of the curve. Experimental results on CT scanned skull data show that proposed algorithm is more robust and accurate at restoring the rigid transformation between two sets of crest line data compared with Iterated Closest Point and Super Curves methods.

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    ABSTRACT: We present a skull assembly and completion framework based on shape matching. In order to assemble fragmented skulls, we need to compute rigid transformations from these fragments to their assembled geometry. We develop a reliable assembly pipeline where each fragment is matched and transformed to be aligned with the template. In order to further complete the assembled skull with several damaged regions, we use the template to repair damaged regions on the assembled skull. The entire pipeline has been conducted on several real skull models and demonstrated great robustness and effectiveness.
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