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Multi-affine registration using local polynomial expansion 被引量:2

Multi-affine registration using local polynomial expansion
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摘要 In this paper, we present a non-linear (multi-affine) registration algorithm based on a local polynomial expansion model. We generalize previous work using a quadratic polynomial expansion model. Local affine models are estimated using this generalized model analytically and iteratively, and combined to a deformable registration algorithm. Experiments show that the affine parameter calculations derived from this quadratic model are more accurate than using a linear model. Experiments further indicate that the multi-affine deformable registration method can handle complex non-linear deformation fields necessary for deformable registration, and a faster convergent rate is verified from our comparison experiment. In this paper, we present a non-linear (multi-affine) registration algorithm based on a local polynomial expansion model. We generalize previous work using a quadratic polynomial expansion model. Local affine models are estimated using this generalized model analytically and iteratively, and combined to a deformable registration algorithm. Experiments show that the anne parameter calculations derived from this quadratic model are more accurate than using a linear model. Experiments further indicate that the multi-affine deformable registration method can handle complex non-linear deformation fields necessary for deformable registration, and a faster convergent rate is verified from our comparison experiment.
出处 《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2010年第7期495-503,共9页 浙江大学学报C辑(计算机与电子(英文版)
基金 supported by the joint PhD Program of the China Scholarship Council(CSC) the US National Institutes of Health(NIH)(Nos.R01MH074794 and P41RR013218) the Na-tional Natural Science Foundation of China(No.60972102)
关键词 Deformable registration Polynomial expansion Least squares Multi-affine Normalized convolution Deformable registration, Polynomial expansion, Least squares, Multi-affine, Normalized convolution
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  • 1Paul Viola,William M. Wells III.Alignment by Maximization of Mutual Information[J].International Journal of Computer Vision.1997(2)

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