期刊
NEUROIMAGE
卷 53, 期 4, 页码 1181-1196出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2010.07.020
关键词
Image registration; Robust statistics; Inverse consistent alignment; Motion correction; Longitudinal analysis
资金
- National Center for Research Resources [P41-RR14075, BIRN002, U24 RR021382]
- National Institute for Biomedical Imaging and Bioengineering [R01 EB006758]
- National Institute on Aging [R01 AG022381, U54 AG024904]
- National Institute for Neurological Disorders and Stroke [R01 NS052585-01, R01 NS042861, P01 NS058793]
- Ellison Medical Foundation
The registration of images is a task that is at the core of many applications in computer vision. In computational neuroimaging where the automated segmentation of brain structures is frequently used to quantify change, a highly accurate registration is necessary for motion correction of images taken in the same session, or across time in longitudinal studies where changes in the images can be expected. This paper, inspired by Nestares and Heeger (2000), presents a method based on robust statistics to register images in the presence of differences, such as jaw movement, differential MR distortions and true anatomical change. The approach we present guarantees inverse consistency (symmetry), can deal with different intensity scales and automatically estimates a sensitivity parameter to detect outlier regions in the images. The resulting registrations are highly accurate due to their ability to ignore outlier regions and show superior robustness with respect to noise, to intensity scaling and outliers when compared to state-of-the-art registration tools such as FLIRT (in FSL) or the coregistration tool in SPM. (C) 2010 Elsevier Inc. All rights reserved.
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