Registration of 3-D images using weighted geometrical features

The authors present a weighted geometrical feature (WGF) registration algorithm. Its efficacy is demonstrated by combining points and a surface. The technique is an extension of Besl and McKay's (1992) iterative closest point (ICP) algorithm. The authors use the WGF algorithm to register X-ray...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 1996-12, Vol.15 (6), p.836-849
Main Authors: Maurer, C.R., Aboutanos, G.B., Dawant, B.M., Maciunas, R.J., Fitzpatrick, J.M.
Format: Article
Language:English
Subjects:
Biological and medical sciences
Clinical trials
Computed tomography
Investigative techniques, diagnostic techniques (general aspects)
Iterative algorithms
Iterative closest point algorithm
Magnetic heads
Magnetic resonance
Medical sciences
Nervous system
Neurosurgery
Radiodiagnosis. Nmr imagery. Nmr spectrometry
Registers
Skull
X-ray imaging
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Summary:The authors present a weighted geometrical feature (WGF) registration algorithm. Its efficacy is demonstrated by combining points and a surface. The technique is an extension of Besl and McKay's (1992) iterative closest point (ICP) algorithm. The authors use the WGF algorithm to register X-ray computed tomography (CT) and T2-weighted magnetic resonance (MR) volume head images acquired from eleven patients that underwent craniotomies in a neurosurgical clinical trial. Each patient had five external markers attached to transcutaneous posts screwed into the outer table of the skull. The authors define registration error as the distance between positions of corresponding markers that are not used for registration. The CT and MR images are registered using fiducial paints (marker positions) only, a surface only, and various weighted combinations of points and a surface. The CT surface is derived from contours corresponding to the inner surface of the skull. The MR surface is derived from contours corresponding to the cerebrospinal fluid (CSF)-dura interface. Registration using points and a surface is found to be significantly more accurate then registration using only points or a surface.
ISSN:0278-0062
1558-254X
DOI:10.1109/42.544501