title: Registration error quantification of a surface-based multimodality image fusion system.
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. Also described is a new semiautomatic surface-based system for registering and visualizing CT and MR images. The registration error of the system was determined 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 nonlinear optimization procedure. Even though the root-mean-square (rms) distance at the registered surfaces was similar to that reported by other groups, it was found that rms distances for the tubes were significantly larger than the final rms distances between the registered surfaces. It was also found that minimizing rms distance at the surface did not minimize rms distance for the tubes.
authors.affiliation: Stanford University Medical Center, Stanford University, California, USA.
authors.forename: P F
authors.initials: PF
authors.lastname: Hemler
authors.forename: S
authors.initials: S
authors.lastname: Napel
authors.forename: T S
authors.initials: TS
authors.lastname: Sumanaweera
authors.forename: R
authors.initials: R
authors.lastname: Pichumani
authors.forename: P A
authors.initials: PA
authors.lastname: van den Elsen
authors.forename: D
authors.initials: D
authors.lastname: Martin
authors.forename: J
authors.initials: J
authors.lastname: Drace
authors.forename: J R
authors.initials: JR
authors.lastname: Adler
authors.forename: I
authors.initials: I
authors.lastname: Perkash
mesh_terms: D055592:Biophysical Phenomena; D001703:Biophysics; D001921:Brain / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D002102:Cadaver; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D008279:Magnetic Resonance Imaging / Q000379:methods / Q000706:statistics & numerical data; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013238:Stereotaxic Techniques; D014057:Tomography, X-Ray Computed / Q000379:methods / Q000706:statistics & numerical data