基于导向滤波器的医学图像融合方法

郭盼; 何文超; 梁龙凯; 张萌; 吕绪浩; 弓馨

doi:10.3788/YJYXS20193406.0605

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基于导向滤波器的医学图像融合方法

图像处理 | 更新时间：2020-07-05

- 基于导向滤波器的医学图像融合方法
- Medical image fusion method based on guided filter
- 液晶与显示 2019年34卷第6期页码：605-612
- 作者机构：
  
  1.东北师范大学人文学院理工学院, 吉林长春 130117
  2.吉林省高校汽车电子技术工程研究中心, 吉林长春 130011
- 作者简介：
  
  [ "郭盼(1989-), 女, 吉林九台人, 讲师, 硕士, 2015年于长春理工大学获得硕士学位, 主要从事数字图像处理及图像融合方面的研究。E-mail:963902263@qq.com" ]
- 基金信息：
  
  吉林省教育厅科学规划项目(No.2016516);吉林省教育厅科学规划项目(No.2016517);吉林省教育厅科学规划项目(No.2016518);Education Department of Jilin Province(No.2016516);Education Department of Jilin Province(No.2016517);Education Department of Jilin Province(No.2016518)
- DOI：10.3788/YJYXS20193406.0605
  中图分类号： TP391.41
- 收稿日期：2018-12-05，
  
  录用日期：2019-3-23，
  
  纸质出版日期：2019-06-05
- 稿件说明：
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郭盼, 何文超, 梁龙凯, 等. 基于导向滤波器的医学图像融合方法[J]. 液晶与显示, 2019,34(6):605-612.

Pan GUO, Wen-chao HE, Long-kai LIANG, et al. Medical image fusion method based on guided filter[J]. Chinese journal of liquid crystals and displays, 2019, 34(6): 605-612.
郭盼, 何文超, 梁龙凯, 等. 基于导向滤波器的医学图像融合方法[J]. 液晶与显示, 2019,34(6):605-612. DOI： 10.3788/YJYXS20193406.0605.

Pan GUO, Wen-chao HE, Long-kai LIANG, et al. Medical image fusion method based on guided filter[J]. Chinese journal of liquid crystals and displays, 2019, 34(6): 605-612. DOI： 10.3788/YJYXS20193406.0605.

摘要

针对医学图像融合过程中易产生人造纹理的问题，本文将引导滤波器应用到医学图像融合中，有效提高融合图像的空间连续性，减少人造纹理的产生。首先利用导向滤波器的细节增强特性获得图像的细节层信息，主要通过导向滤波器对原图像进行滤波处理得到其基础层图像信息，再与原图像作差运算即可得细节层图像信息；然后采用基于含可变参数

的邻域统计特性融合规则得到图像融合权值系数，该融合规则在原有的邻域统计特性中加入可变参数

，可以有效增强融合图像的细节信息；最后将原图像按照所得的图像融合权值系数进行融合，得到最终的融合图像。实验仿真结果对比分析表明，随着可变参数

的变化，融合效果也有明显的提高，当

10后，融合效果基本趋于平稳。该融合方法可以有效地实现医学图像融合，与其他融合方法相比，具有一定的优越性。

Abstract

Aiming at the problem that artificial texture is easy to be produced in the process of medical image fusion

the guiding filter was applied to medical image fusion to effectively improve the spatial continuity of fusion image and reduce the generation of artificial texture. Firstly

the detail layer information of the image was obtained by using the detail enhancement characteristic of the guide filter. The basic layer image information was obtained by filtering the original image through the guide filter

and then the detail layer image information could be obtained by the difference operation between the original image and the guide filter. Then

the image fusion weight coefficient was obtained based on the neighborhood statistical characteristics fusion rule with variable parameter

. The fusion rule added variable parameter

to the original neighborhood statistical characteristics

which could effectively enhance the details of the fusion image. Finally

the original image was fused according to the obtained image fusion weight coefficient to obtain the final fusion image. The comparison and analysis of experimental simulation results indicate that the fusion effect is significantly improved with the change of variable parameter

. The fusion effect is basically stable after

10. This fusion algorithm can effectively realize medical image fusion and has certain advantages over other fusion methods.

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references

刘帅奇, 郑伟, 赵杰, 等.数字图像融合算法分析与应用[M].北京:机械工业出版社, 2018:4.

LIU S Q, ZHENG W, ZHAO J, et al . Analysis and Application of Algorithm for Digital Image Fusion [M]. Beijing:China Machine Press, 2018:4. (in Chinese)

杨波.基于小波的像素级图像融合算法研究[D].上海: 上海交通大学, 2008.

YANG B. Research on wavelet-based pixel-level image fusion algorithms[D]. Shanghai: Shanghai Jiao Tong University, 2008. (in Chinese)

LEE DD, SEUNG H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature , 1999, 401(6755):788-791.

LEE DD, SEBASTIAN S H. Algorithms for non-negative matrix factorization[J]. Advances in Neural Information Processing Systems , 2001, 13:556-562.

SINGH R, VATSA M, NOORE A. Multimodal medical image fusion using redundant discrete wavelet transform[C]// Proceedings of 2009 Seventh International Conference on Advances in Pattern Recognition . Kolkata, India: IEEE, 2009.

DACUNHA A L, ZHOU J, DO M N. The nonsubsampled contourlet transform:Theory, design, and applications[J]. IEEE Transactions on Image Processing , 2006, 15(10):3089-3101.

XING X X, LIU F, SHANG W W, et al . Medical image fusion in compressed sensing based on non-subsampled contourlet transform[C]// Proceedings of 2013 IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks . Dalian, China: IEEE, 2013.

王雷, 李彬, 田联房.基于平移不变剪切波变换的医学图像融合[J].华南理工大学学报:自然科学版, 2011, 39(12):13-19.

WANG L, LI B, TIAN L F. Medical image fusion based on shift-invariant shearlet transformation[J]. Journal of South China University of Technology : Natural Science Edition , 2011, 39(12):13-19. (in Chinese)

方静, 罗高鹏.改进快速NSST的热烟羽红外与可见光图像融合[J].激光与红外, 2017, 47(7):914-920.

FANG J, LUO G P. Infrared and visible images fusion of thermal plume based on rapid non-subsampled shearlet transform[J]. Laser & Infrared , 2017, 47(7):914-920. (in Chinese)

葛雯, 姬鹏冲, 赵天臣. NSST域改进NMF的红外与可见光图像融合[J].光电工程, 2016, 43(4):73-77, 83.

GE W, JI P C, ZHAO T C. Infrared and visible light images fusion of improved NMF on NSST domain[J]. Opto - Electronic Engineering , 2016, 43(4):73-77, 83. (in Chinese)

邢笑雪.基于NSST的图像融合算法研究[D].长春: 吉林大学, 2014.

XING X X. Research on the image fusion algorithm based on Non-Sumsampled Shearlet Transform[D]. Changchun: Jilin University, 2014. (in Chinese)

HE K M, SUN J, TANG X O. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2013, 35(6):1397-1409.

刘帅奇, 胡绍海, 赵杰, 等.结合向导滤波与复轮廓波变换的多聚焦图像融合算法[J].信号处理, 2016, 32(3):276-286.

LIU S Q, HU S H, ZHAO J, et al . Multi-focus image fusion using guided filtering and complex contourlet transform[J]. Journal of Signal Processing , 2016, 32(3):276-286. (in Chinese)

LIU S Q, SHI M Z, ZHU Z H, et al . Image fusion based on complex-shearlet domain with guided filtering[J]. Multidimensional Systems and Signal Processing , 2017, 28(1):207-224.

SHAH P, MERCHANT S M, DESAI U B. An efficient adaptive fusion scheme for multifocus images in wavelet domain using statistical properties of neighborhood[C]// Proceedings of the 14th International Conference on Information Fusion . Chicago, USA: IEEE, 2011.

KUMAR B K S. Image fusion based on pixel significance using cross bilateral filter[J]. Signal , Image and Video Processing , 2015, 9(5):1193-1204.

BAVIRISETTI D P, KOLLU V, GANG X, et al . Fusion of MRI and CT images using guided image filter and image statistics[J]. International Journal of Imaging Systems and Technology , 2017, 27(3):227-237.

刘莹.图像纹理的特征提取和分类方法研究[D].武汉: 华中科技大学, 2013.

LIU Y. Research on the method of image texture feature extraction and classification[D]. Wuhan: Huazhong University of Science and Technology, 2013. (in Chinese)

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