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一种用于图像配准的具有局部自适应正则化的恶魔算法。

A Demons algorithm for image registration with locally adaptive regularization.

作者信息

Cahill Nathan D, Noble J Alison, Hawkes David J

机构信息

Institute of Biomedical Engineering, University of Oxford, Oxford, UK.

出版信息

Med Image Comput Comput Assist Interv. 2009;12(Pt 1):574-81. doi: 10.1007/978-3-642-04268-3_71.

Abstract

Thirion's Demons is a popular algorithm for nonrigid image registration because of its linear computational complexity and ease of implementation. It approximately solves the diffusion registration problem by successively estimating force vectors that drive the deformation toward alignment and smoothing the force vectors by Gaussian convolution. In this article, we show how the Demons algorithm can be generalized to allow image-driven locally adaptive regularization in a manner that preserves both the linear complexity and ease of implementation of the original Demons algorithm. We show that the proposed algorithm exhibits lower target registration error and requires less computational effort than the original Demons algorithm on the registration of serial chest CT scans of patients with lung nodules.

摘要

Thirion 的恶魔算法是一种用于非刚性图像配准的流行算法,因其线性计算复杂度和易于实现。它通过依次估计驱动变形趋向对齐的力向量,并通过高斯卷积对力向量进行平滑,近似地解决了扩散配准问题。在本文中,我们展示了如何将恶魔算法进行推广,以允许以一种保留原始恶魔算法的线性复杂度和易于实现的方式进行图像驱动的局部自适应正则化。我们表明,在对患有肺结节的患者的胸部序列 CT 扫描进行配准时,所提出的算法比原始恶魔算法具有更低的目标配准误差,并且需要更少的计算量。

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