基于逐点互信息的多模态差胚恶魔配准

Huanxiang Lu, M. Reyes, Amira Serijovic, S. Weber, Y. Sakurai, H. Yamagata, P. Cattin
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引用次数: 40

摘要

本文提出了一种基于差分同胚算法的多模态图像配准变分方法。差分同形图像是一种鲁棒且有效的图像配准方法。然而,主要的缺点是它不能处理多模态。我们建议用能量函数中的逐点互信息(PMI)取代标准的图像相似性度量(图像强度差异)。通过比较我们基于PMI的微分同构图像和基于b样条的自由变形方法(FFD)在模拟变形上的精度,我们表明我们提出的算法表现得更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-modal diffeomorphic demons registration based on point-wise mutual information
In this paper we propose a variational approach for multimodal image registration based on the diffeomorphic demons algorithm. Diffeomorphic demons has proven to be a robust and efficient way for intensity-based image registration. However, the main drawback is that it cannot deal with multiple modalities. We propose to replace the standard demons similarity metric (image intensity differences) by point-wise mutual information (PMI) in the energy function. By comparing the accuracy between our PMI based diffeomorphic demons and the B-Spline based free-form deformation approach (FFD) on simulated deformations, we show the proposed algorithm performs significantly better.
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