CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2001 vol.23 Issue No.02 - February
Issue No.02 - February (2001 vol.23)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.908967
<p><b>Abstract</b>—Accurate and robust correspondence calculations are very important in many medical and biological applications. Often, the correspondence calculation forms part of a rigid registration algorithm, but accurate correspondences are especially important for elastic registration algorithms and for quantifying changes over time. In this paper, a new correspondence calculation algorithm, CSM (Correspondence by Sensitivity to Movement), is described. A robust corresponding point is calculated by determining the sensitivity of a correspondence to movement of the point being matched. If the correspondence is reliable, a perturbation in the position of this point should not result in a large movement of the correspondence. A measure of reliability is also calculated. This correspondence calculation method is independent of the registration transformation and has been incorporated into both a 2D elastic registration algorithm for warping serial sections and a 3D rigid registration algorithm for registering pre and postoperative facial range scans. These applications use different methods for calculating the registration transformation and accurate rigid and elastic alignment of images has been achieved with the CSM method. It is expected that this method will be applicable to many different applications and that good results would be achieved if it were to be inserted into other methods for calculating a registration transformation from correspondences.</p>
Image registration, iterative closest point, surface matching, point correspondence, image warping, image matching, serial sections, reconstruction.
Elizabeth Guest, Elizabeth Berry, Richard A. Baldock, Márta Fidrich, Mike A. Smith, "Robust Point Correspondence Applied to Two-and Three-Dimensional Image Registration", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.23, no. 2, pp. 165-179, February 2001, doi:10.1109/34.908967