A1 Refereed original research article in a scientific journal
A FULLY-AUTOMATIC MULTIMODALITY IMAGE REGISTRATION ALGORITHM
Authors: ARDEKANI BA, BRAUN M, HUTTON BF, KANNO I, IIDA H
Publisher: LIPPINCOTT-RAVEN PUBL
Publication year: 1995
Journal: Journal of Computer Assisted Tomography
Journal name in source: JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY
Journal acronym: J COMPUT ASSIST TOMO
Volume: 19
Issue: 4
First page : 615
Last page: 623
Number of pages: 9
ISSN: 0363-8715
DOI: https://doi.org/10.1097/00004728-199507000-00022
Abstract
Objective: A fully automatic multimodality image registration algorithm is presented, The method is primarily designed for 3D registration of MR and PET images of the brain. However, it has also been successfully applied to CT-PET, MR-CT, and MR-SPECT registrations.Materials and Methods: The head contour is detected on the MR image using a gradient threshold method. The head region in the MR image is then segmented into a set of connected components using the K-means clustering algorithm. When the two image sets are registered, the segmentation of the MR image indirectly generates a segmentation of the PET image. The best registration is taken to be the one that optimizes the segmentation induced on the PET image. In this article, the K-means minimum variance criterion is used as a cost function, and the optimization is performed using the method of coordinate descent.Results: The algorithm was tested on 80 H-2 O-15 PET and MR image pairs from 10 subjects. Qualitatively correct results were obtained in all cases. With use of external markers visible in both image modalities, the average registration error was estimated to be <3 mm.Conclusion: The algorithm presented in this article requires no user interaction and can be applied to a wide range of registration problems. Quantitative and qualitative evaluations of the algorithm indicate a high degree of accuracy.
Objective: A fully automatic multimodality image registration algorithm is presented, The method is primarily designed for 3D registration of MR and PET images of the brain. However, it has also been successfully applied to CT-PET, MR-CT, and MR-SPECT registrations.Materials and Methods: The head contour is detected on the MR image using a gradient threshold method. The head region in the MR image is then segmented into a set of connected components using the K-means clustering algorithm. When the two image sets are registered, the segmentation of the MR image indirectly generates a segmentation of the PET image. The best registration is taken to be the one that optimizes the segmentation induced on the PET image. In this article, the K-means minimum variance criterion is used as a cost function, and the optimization is performed using the method of coordinate descent.Results: The algorithm was tested on 80 H-2 O-15 PET and MR image pairs from 10 subjects. Qualitatively correct results were obtained in all cases. With use of external markers visible in both image modalities, the average registration error was estimated to be <3 mm.Conclusion: The algorithm presented in this article requires no user interaction and can be applied to a wide range of registration problems. Quantitative and qualitative evaluations of the algorithm indicate a high degree of accuracy.
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