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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Asymmetric and symmetric unbiased image registration: Statistical assessment of performance
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Igor Yanovsky, Department of Mathematics, University of California, Los Angeles, 90095, USA
Paul M. Thompson, Laboratory of Neuro Imaging, UCLA School of Medicine, 90095, USA
Stanley Osher, Department of Mathematics, University of California, Los Angeles, 90095, USA
Alex D. Leow, Laboratory of Neuro Imaging, UCLA School of Medicine, 90095, USA
Measures of brain changes can be computed from sequential MRI scans, providing valuable information on disease progression for neuroscientific studies and clinical trials. Tensor-based morphometry (TBM) creates maps of these brain changes, visualizing the 3D profile and rates of tissue growth or atrophy. In this paper, we examine the power of different nonrigid registration models to detect changes in TBM, and their stability when no real changes are present. Specifically, we investigate an asymmetric version of a recently proposed unbiased registration method, using mutual information as the matching criterion. We compare matching functionals (sum of squared differences and mutual information), as well as large deformation registration schemes (viscous fluid registration versus symmetric and asymmetric unbiased registration) for detecting changes in serial MRI scans of 10 elderly normal subjects and 10 patients with Alzheimer’s Disease scanned at 2-week and 1-year intervals. We demonstrated that the unbiased methods, both symmetric and asymmetric, have higher reproducibility. The unbiased methods were also less likely to detect changes in the absence of any real physiological change. Moreover, they measured biological deformations more accurately by penalizing bias in the corresponding statistical maps.
Citation:
Igor Yanovsky, Paul M. Thompson, Stanley Osher, Alex D. Leow, "Asymmetric and symmetric unbiased image registration: Statistical assessment of performance," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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