Abstract
In this work, we present a method for the integration of fea- ture and intensity information for non rigid registration. Our method is based on a free-form deformation model, and uses a normalized mu- tual information intensity similarity metric to match intensities and the robust point matching framework to estimate feature (point) correspon- dences. The intensity and feature components of the registration are posed in a single energy functional with associated weights. We com- pare our method to both point-based and intensity-based registrations. In particular, we evaluate registration accuracy as measured by point landmark distances and image intensity similarity on a set of seventeen normal subjects. These results suggest that the integration of intensity and point-based registration is highly effective in yielding more accurate registrations.
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Papademetris, X., Jackowski, A.P., Schultz, R.T., Staib, L.H., Duncan, J.S. (2004). Integrated Intensity and Point-Feature Nonrigid Registration. In: Barillot, C., Haynor, D.R., Hellier, P. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. MICCAI 2004. Lecture Notes in Computer Science, vol 3216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30135-6_93
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DOI: https://doi.org/10.1007/978-3-540-30135-6_93
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