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Learning-based deformation estimation for fast non-rigid registration

Title
Learning-based deformation estimation for fast non-rigid registration
Authors
Kim M.-J.Kim M.-H.Shen D.
Ewha Authors
김명희
SCOPUS Author ID
김명희scopus
Issue Date
2008
Journal Title
2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops
Indexed
SCOPUS scopus
Abstract
This paper presents a learning-based deformation estimation method for fast non-rigid registration. First, a PCA-based statistical deformation model is constructed using the deformation fields obtained by conventional registration algorithms between a template image and training subject images. Then, the constructed statistical model is used to generate a large number of sample deformation fields by resampling in the PCA space. In the meanwhile, by warping the template using these sample deformation fields, the respective sample images in the PCA space can be also generated. Finally, after learning the correlation between the features of the sample images and their deformation coefficients, given a new test image, we can immediately estimate its relative deformations to the template based on its image information. Using this estimated deformation, we can warp the template to generate an intermediate template close to the test image. Since the intermediate template is more similar to the test image compared to the original template, the registration via the intermediate template becomes much easier and faster. Experimental results show that the proposed learning-based registration method can fast register MR brain image with robust performance. © 2008 IEEE.
DOI
10.1109/CVPRW.2008.4563006
ISBN
9781424423408
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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