Registration Error Quantification of a Surface-Based Multimodality Image Fusion System

Reference: Hemler, P. F.; Napel, S.; Sumanaweera, T. S.; Pichumani, R.; Elsen, P. A. v. d.; Martin, D.; Drace, J.; Perkash, I.; & Adler, J. R. Registration Error Quantification of a Surface-Based Multimodality Image Fusion System. Knowledge Systems Laboratory, Medical Computer Science, October, 1994.

Abstract: This paper presents a new reference data set and associated quantification methodology to assess the accuracy of registration of Computerized Tomography (CT) and Magnetic Resonance (MR) images. We also describe a new semi-automatic surface-based system for registering and visualizing CT and MR images. We determined the registration error of our system using a reference data set that was obtained from a cadaver in which rigid fiducial tubes were inserted prior to imaging. Registration error was measured as the distance between an analytic expression for each fiducial tube in one image set and transformed samples of the corresponding tube obtained from the other. Registration was accomplished by first identifying surfaces of similar anatomic structures in each image set. A transformation that best registered these structures was determined using a non-linear optimization procedure. Even though the root-mean-square (RMS) distance at the registered surfaces was similar to that reported by other groups, we found that RMS distances for the tubes was significantly larger than the final RMS distances between the registered surfaces. We also found that minimizing RMS distance at the skin surface did not minimize RMS distance for the tubes.

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