Jojo Liu | Beihang University (original) (raw)
Uploads
Papers by Jojo Liu
Journal of computer …, Jan 1, 1995
A fully automatic multimodality image registration algorithm is presented. The method is primaril... more 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. 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. The algorithm was tested on 80 H2 15O 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. 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.
Bookmarks Related papers MentionsView impact
Medical Image Computing and Computer …, Jan 1, 2000
Bookmarks Related papers MentionsView impact
Journal of computer …, Jan 1, 1998
We sought to describe and validate an automated image registration method (AIR 3.0) based on matc... more We sought to describe and validate an automated image registration method (AIR 3.0) based on matching of voxel intensities. Different cost functions, different minimization methods, and various sampling, smoothing, and editing strategies were compared. Internal consistency measures were used to place limits on registration accuracy for MRI data, and absolute accuracy was measured using a brain phantom for PET data. All strategies were consistent with subvoxel accuracy for intrasubject, intramodality registration. Estimated accuracy of registration of structural MRI images was in the 75 to 150 microns range. Sparse data sampling strategies reduced registration times to minutes with only modest loss of accuracy. The registration algorithm described is a robust and flexible tool that can be used to address a variety of image registration problems. Registration strategies can be tailored to meet different needs by optimizing tradeoffs between speed and accuracy.
Bookmarks Related papers MentionsView impact
ACM computing surveys (CSUR), Jan 1, 1992
Bookmarks Related papers MentionsView impact
Medical Imaging, …, Jan 1, 1997
Bookmarks Related papers MentionsView impact
Journal of computer …, Jan 1, 1995
A fully automatic multimodality image registration algorithm is presented. The method is primaril... more 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. 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. The algorithm was tested on 80 H2 15O 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. 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.
Bookmarks Related papers MentionsView impact
Medical Image Computing and Computer …, Jan 1, 2000
Bookmarks Related papers MentionsView impact
Journal of computer …, Jan 1, 1998
We sought to describe and validate an automated image registration method (AIR 3.0) based on matc... more We sought to describe and validate an automated image registration method (AIR 3.0) based on matching of voxel intensities. Different cost functions, different minimization methods, and various sampling, smoothing, and editing strategies were compared. Internal consistency measures were used to place limits on registration accuracy for MRI data, and absolute accuracy was measured using a brain phantom for PET data. All strategies were consistent with subvoxel accuracy for intrasubject, intramodality registration. Estimated accuracy of registration of structural MRI images was in the 75 to 150 microns range. Sparse data sampling strategies reduced registration times to minutes with only modest loss of accuracy. The registration algorithm described is a robust and flexible tool that can be used to address a variety of image registration problems. Registration strategies can be tailored to meet different needs by optimizing tradeoffs between speed and accuracy.
Bookmarks Related papers MentionsView impact
ACM computing surveys (CSUR), Jan 1, 1992
Bookmarks Related papers MentionsView impact
Medical Imaging, …, Jan 1, 1997
Bookmarks Related papers MentionsView impact