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

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Citation: Paul F. Hemler and Sandy Napel and Thilaka S. Sumanaweera and Ramani Pichumani and Petra A. van den Elsen and David L. Martin and John Drace and Inder Perkash and John R. Adler. (1994) Registration Error Quantification of a Surface-Based Multimodality Image Fusion System. In KSL-94-66, October,1994.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Paul F. Hemler and Sandy Napel and Thilaka S. Sumanaweera and Ramani Pichumani and Petra A. van den Elsen and David L. Martin and John Drace and Inder Perkash and John R. Adler
title Registration Error Quantification of a Surface-Based Multimodality Image Fusion System
number KSL-94-66
institution Knowledge Systems, AI Laboratory
year 1994
month October
Bibtex more
note Medical Computer Science
Access Paper
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 analytic expression for each fiducial tube in one image set and transformed samples ofsthe 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 anon-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.

KSL Technical Report ID: KSL-94-66
Facts about Registration Error Quantification of a Surface-Based Multimodality Image Fusion SystemRDF feed
Abstract This paper presents a new reference data s … 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 analytic expression for each fiducial tube in one image set and transformed samples ofsthe 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 anon-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. d not minimize RMS distance for the tubes.
Author Paul F. Hemler and Sandy Napel and Thilaka S. Sumanaweera and Ramani Pichumani and Petra A. van den Elsen and David L. Martin and John Drace and Inder Perkash and John R. Adler  +
Bibtype techreport  +
Has author Paul F. Hemler and Sandy Napel and Thilaka S. Sumanaweera and Ramani Pichumani and Petra A. van den Elsen and David L. Martin and John Drace and Inder Perkash and John R. Adler  +
Has identifier KSL-94-66  +
Has publishing details October,1994  +
Has title Registration Error Quantification of a Surface-Based Multimodality Image Fusion System  +
Has where published KSL-94-66  +
Has year 1994  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-94-66  +
Month October  +
Note Medical Computer Science
Number KSL-94-66  +
Process note NO  +
Title Registration Error Quantification of a Surface-Based Multimodality Image Fusion System  +
Year 1994  +
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