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基于单步 DFT 结合相位相关约束的多模态脑图像快速亚像素配准算法。

A Fast Subpixel Registration Algorithm Based on Single-Step DFT Combined with Phase Correlation Constraint in Multimodality Brain Image.

机构信息

People's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang 830002, China.

Xinjiang Uygur Autonomous Region Hospital of Traditional Chinese Medicine, Xinjiang 831199, China.

出版信息

Comput Math Methods Med. 2020 May 7;2020:9343461. doi: 10.1155/2020/9343461. eCollection 2020.

Abstract

Multimodality brain image registration technology is the key technology to determine the accuracy and speed of brain diagnosis and treatment. In order to achieve high-precision image registration, a fast subpixel registration algorithm based on single-step DFT combined with phase correlation constraint in multimodality brain image was proposed in this paper. Firstly, the coarse positioning at the pixel level was achieved by using the downsampling cross-correlation model, which reduced the Fourier transform dimension of the cross-correlation matrix and the multiplication of the discrete Fourier transform matrix, so as to speed up the coarse registration process. Then, the improved DFT multiplier of the matrix multiplication was used in the neighborhood of the coarse point, and the subpixel fast location was achieved by the bidirectional search strategy. Qualitative and quantitative simulation experiment results show that, compared with comparison registration algorithms, our proposed algorithm could greatly reduce space and time complexity without losing accuracy.

摘要

多模态脑图像配准技术是脑诊断和治疗准确性和速度的关键技术。为了实现高精度的图像配准,本文提出了一种基于单步 DFT 与多模态脑图像相位相关约束相结合的快速亚像素配准算法。首先,利用下采样互相关模型实现像素级的粗定位,降低了互相关矩阵的傅里叶变换维度和离散傅里叶变换矩阵的乘法次数,从而加快了粗配准过程。然后,在粗定位点的邻域内使用改进的矩阵乘法 DFT 乘法器,通过双向搜索策略实现亚像素快速定位。定性和定量的仿真实验结果表明,与比较的配准算法相比,所提出的算法在不损失精度的情况下可以大大降低空间和时间复杂度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c73/7229540/b261e4c91303/CMMM2020-9343461.001.jpg

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