基于图像特征和光流场的非刚性图像配准

纪慧中; 贾大宇; 董恩清; 薛鹏; 唐振超

doi:10.3788/OPE.20172509.2469

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基于图像特征和光流场的非刚性图像配准

信息科学 | 更新时间：2020-08-13

- 基于图像特征和光流场的非刚性图像配准
- Non-rigid registrations based on image characteristics and optical flows
- 光学精密工程 2017年25卷第9期页码：2469-2482
- 作者机构：
  
  山东大学(威海) 机电与信息工程学院, 山东威海 264200
- 作者简介：
  
  [ "纪慧中(1995-), 女, 湖南长沙人, 硕博连读研究生, 2016年于哈尔滨工业大学(威海)获得学士学位, 主要从事医学图像处理的研究.E-mail:jiuizhong131@qq.com" ]
  [ "董恩清(1967-), 男, 辽宁营口人, 教授, 博士生导师, 1987年于中国矿业大学获学士学位, 1993年于长安大学获硕士学位, 2002年于西安交通大学获得信息与通信工程专业博士学位, 主要从事无线通信技术, 无线传感器网络, 医学图像处理等的研究.E-mail:enqdong@sdu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金资助项目(81671848);国家自然科学基金资助项目(81371635);山东省重点研发计划资助项目(2016GGX101017)
- DOI：10.3788/OPE.20172509.2469
  中图分类号： TP391.4
- 收稿日期：2017-03-06，
  
  录用日期：2017-5-28，
  
  纸质出版日期：2017-09-25
- 稿件说明：
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纪慧中, 贾大宇, 董恩清, 等. 基于图像特征和光流场的非刚性图像配准[J]. 光学精密工程, 2017,25(9):2469-2482.

Hui-zhong JI, Da-yu JIA, En-qing DONG, et al. Non-rigid registrations based on image characteristics and optical flows[J]. Optics and precision engineering, 2017, 25(9): 2469-2482.
纪慧中, 贾大宇, 董恩清, 等. 基于图像特征和光流场的非刚性图像配准[J]. 光学精密工程, 2017,25(9):2469-2482. DOI： 10.3788/OPE.20172509.2469.

Hui-zhong JI, Da-yu JIA, En-qing DONG, et al. Non-rigid registrations based on image characteristics and optical flows[J]. Optics and precision engineering, 2017, 25(9): 2469-2482. DOI： 10.3788/OPE.20172509.2469.

摘要

考虑传统非刚性图像配准方法无法同时满足配准精度和配准时间要求，综合图像的特征和灰度信息，提出了几种改进的非刚性图像配准方法：基于圆形描述子特征的非刚性配准方法（Circle Descriptor Feature，CDF），基于动态驱动力Demons的非刚性配准方法（Dynamic Driving Force Demons，DDFD），和基于图像特征和光流场的非刚性配准方法。CDF方法通过提取图像的特征点，采用圆形描述子代替传统方法的正方形描述子来保证图像的旋转不变性，提高配准速度；DDFD方法通过引入驱动力系数动态改变驱动力，有效地解决了传统方法配准时间和配准精度低的问题；基于图像特征和光流场的非刚性配准方法则首先提取浮动图像和参考图像的特征点，然后利用提取的特征点进行粗配准（特征级配准），再采用基于光流场的方法进行精细配准（像素级配准），最终实现配准精度和配准时间的兼顾。对checkboard测试图像、自然图像、脑部MR图像、肝部CT图像进行了实验测试，结果表明，本文方法在配准时间、配准精度及对大形变图像的适应性方面均优于传统尺度不变特征转换（SIFT）、加速鲁棒特征（SURF）、Demons、Active Demons和全变差正则项-L

范数项（TV-L

）等方法。

Abstract

As the non-rigid image registration methods can not meet the requirements of registration accuracy and registration time simultaneously

three kinds of improved non-rigid registration methods are proposed based on image characteristics and image gray. These non-rigid registration methods were based on the Circle Descripto increases Feature (CDF)

Dynamic Driving Force Demons (DDFD) and image characteristics and optical flow

respectively. In CDF method

feature points were extracted from the images

and the circle descriptor is used in the method instead of square descriptor in classical methods

by which the rotation invariance was maintained and the speed of the registration was increased. In DDFD method

the driving force was changed by introducing the driving force coefficient

so that the registration time and registration accuracy were improved effectively. In registration methods based on image characteristics and optical flow

the feature points were extracted from a float image and a reference image by using registration method based on image characteristics

and these extracted feature points were used to get a coarse registered image (feature level registration); then the optical-flow method was used to register accurately (pixel level registration) for the coarse registered image and to achieves the purpose of taking account of the registration accuracy and registration time. The experiments on checkboard images

natural images

brain MR images and liver CT images were performed and the results show that the proposed methods are better than the classical methods such as Scale-invariant Feature Transform (SIFT)

Speeded-Up Robust Features(SURF)

Demons

Active Demons and Total Variation Regularization/L

norm (TV-L

) in registration time

registration accuracy and adaptability for large-deformation images.

关键词

Keywords

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