大形变微分同胚图像配准快速算法

闫德勤; 刘彩凤; 刘胜蓝; 刘德山

doi:10.16383/j.aas.2015.c140816

大形变微分同胚图像配准快速算法

doi: 10.16383/j.aas.2015.c140816

1.
辽宁师范大学计算机与信息技术学院大连 116029;
2.
大连理工大学电子信息与电气工程学部计算机科学与技术学院大连 116024

基金项目:

国家自然科学基金(61105085, 61373127, 61170143), 辽宁省教育厅基金(L2014427)资助

详细信息

作者简介:
刘彩凤辽宁师范大学计算机与信息学院硕士研究生.主要研究方向为信息处理与模式识别.E-mail:liucaifeng12345@163.com

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出版历程
- 收稿日期: 2014-11-26
- 修回日期: 2015-04-23
- 刊出日期: 2015-08-20

A Fast Image Registration Algorithm for Diffeomorphic Image with Large Deformation

1.
College of Computer and Information Technology, Liaoning Normal University, Dalian 116029;
2.
School of Computer Science and Technology, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024

Funds:

Supported by National Natural Science Foundation of China (61105085, 61373127, 61170143) and Science Foundation of Education Ministry of Liaoning Province (L2014427)

摘要

摘要: 本文提出一种研究大形变图像配准算法. 大形变使得图像信息和拓扑结构有较大的改变, 目前该方面的研究仍然是一个难点. 基于严密数学理论的微分同胚Demons算法是图像配准的著名算法, 为解决大形变配准问题提供了重要基础. 基于对微分同胚Demons算法的研究结合流形学习的思想提出一种大形变图像配准的新算法(MRL算法). 新算法通过挖掘图像的局部和全局流形信息改进微分同胚Demons 速度场的更新, 更好地保持图像的拓扑结构. 对比实验结果表明, 本文所提出的算法能够快速高精度地实现大形变图像的配准.
- 图像配准 /
- 微分同胚Demons /
- 流形 /
- 大形变
Abstract: A registration algorithm for large deformation images is porposed. Since image information and topological structure undergo great changes with large deformation, image registration for large deformation images is a challenging work. The diffeomorphic demons algorithm, based on strict mathematical theory, is a famous image registration algorithm, which provides an important basis to solve the problem of large deformation image registration. Based on the study of the diffeomorphic demons algorithm, by combining the ideas of manifold learning, this paper presents a new algorithm for large deformation image registration (called MRL). The new proposed algorithm improves the diffeomorphic demons velocity field up by capturing both local and global manifold information of the image, and better maintains the topology of the image. Comparative experiment results show that the algorithm can quickly realize large deformation registration with a higher precision.
- Image registration /
- diffeomorphic demons /
- manifold learning /
- large deformation

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