Ms. Ting Guo Profile

Ting Guo

at Robarts Research Institute

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Proceedings Article | 12 April 2005 Paper

Evaluation and validation methods for intersubject nonrigid 3D image registration of the human brain

Ting Guo, Yves Starreveld, Terry Peters

Proceedings Volume 5744, (2005) https://doi.org/10.1117/12.594082

KEYWORDS: Image registration, Brain, Neuroimaging, Optical inspection, 3D image processing, Algorithm development, Databases, Medical imaging, Visualization, Magnetic resonance imaging

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This work presents methodologies for assessing the accuracy of non-rigid intersubject registration algorithms from both qualitative and quantitative perspectives. The first method was based on a set of 43 anatomical landmarks. MRI brain images of 12 subjects were non-rigidly registered to the standard MRI dataset. The “gold-standard” coordinates of the 43 landmarks in the target were estimated by averaging their coordinates after 6 tagging sessions. The Euclidean distance between each landmark of a subject after warping to the reference space and the homologous “gold-standard” landmark on the reference image was considered as the registration error. Another method based on visual inspection software displaying the spatial change of colour-coded spheres, before and after warping, was also developed to evaluate the performance of the non-rigid warping algorithms within the homogeneous regions in the deep-brain. Our methods were exemplified by assessing and comparing the accuracy of two intersubject non-rigid registration approaches, AtamaiWarp and ANIMAL algorithms. From the first method, the average registration error was 1.04mm +/- 0.65mm for AtamaiWarp, and 1.59mm +/- 1.47mm for ANIMAL. With maximum registration errors of 2.78mm and 3.90mm respectively, AtamaiWarp and ANIMAL located 58% and 35% landmarks respectively with registration errors less than 1mm. A paired t-test showed that the differences in registration error between AtamaiWarp and ANIMAL were significant (P < 0.002) demonstrating that AtamaiWarp, in addition to being over 60 times faster than ANIMAL, also provides more accurate results. From the second method, both algorithms treated the interior of homogeneous regions in an appropriate manner.

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