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Interpolation-based nonrigid deformation estimation under manifold regularization constraint
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DOI：10.1016/j.patcog.2022.108695.png)
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Abstract
En 中文
This paper addresses the image/surface deformation problem by estimating interpolation functions pixel by pixel(or voxel by voxel) between control point pairs using labeled control points and unlabeled feature points as input. The labeled control points are usually selected by users and labeled through user operations; the unlabeled feature points are extracted from the source image. We formulate the interpolation function estimation at each pixel as a weighted semi-supervised learning problem. Specially, we employ moving least squares to estimate the nonrigid deformation function according to the weights between each pixel and the labeled control points and exploit manifold regularization to preserve the intrinsic geometric information of the unlabeled feature points contained in the object. Moreover, we define the nonrigid deformation function in a reproducing kernel Hilbert space to derive a closed-form solution. To reduce the computational complexity, we also adopt a sparse approximation to realize a fast implementation. It is worth mentioning that our proposed method is a unified framework with two different basis functions. Both basis-function-based methods are applied to 2D image deformation, 3D surface deformation, and medical image registration. Extensive experiments on the data and the resulting mean opinion score (MOS) on the 2D deformation demonstrate that our methods are superior to state-of-the-art ones. (c) 2022 Elsevier Ltd. All rights reserved.
Keywords:
Nonrigid deformation
Manifold regularization
Gaussian kernel
TPS kernel
Medical image registration
Journal
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7.6
Papers:
1.3W
Citations:
4.5W
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