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基于小波变换医学图像融合算法的对比分析 被引量:18

Comparison and Analysis of Medical Image Fusion Algorithms Based on Wavelet Transform
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摘要 小波变换融合方法具有重要的应用价值,而融合规则的选取直接影响着融合效果。为了获得医学临床上实用的小波融合算法,选择标准CT/MRI图像,通过调整和组合各种小波变换低频及高频融合规则进行仿真实验,深入对比分析各种融合规则对医学图像融合性能的影响。在此基础上,提出低频能量取大与高频系数绝对值取大相结合的融合改进算法,比目前基于传统小波融合规则的融合质量及各项客观评价指标都有明显提高,在各种算法比较中最优。采用多聚焦图像和临床实际的CT/MRI图像进行对比验证,表明了方法的有效性。理论分析和实验结果证明:选取合适的融合规则对融合结果影响很大,本研究提出的算法简单有效。 The image fusion method of wavelet transform has important application value.Different choices of fusion rules directly affect the fusion results.In order to obtain practical wavelet transform fusion algorithm in clinic,simulation study were conducted by choosing standard CT and MRI images,adjusting and combining various fusion rules of the wavelet transform low frequency and high frequency.Comparing the traditional wavelet transform algorithm adopting the"average"rule to low-frequency and the"coefficient absolute value"to high-frequency,an efficient fusion method was proposed which the"region energy"fusion rule was adapted to the low-frequency coefficients and the"region variance"fusion rule to the high-frequency coefficients.Then the further research has been made and the better algorithm which the"region energy"and the"coefficient absolute value" fusion rules were used as the low-frequency and the high-frequency coefficients individually has been introduced.And then the compared experiments were done to test and verify the results using multi-focus images and clinical CT /MRI images.Theoretical analysis and experimental results demonstrated that appropriate fusion rules influence on the image fusion results and the proposed algorithm of image fusion is effective.
出处 《中国生物医学工程学报》 CAS CSCD 北大核心 2011年第2期196-205,共10页 Chinese Journal of Biomedical Engineering
基金 江苏省高校科技成果转化项目(JHZD09-22) 徐州市科技计划项目(XM09B070)
关键词 图像融合 小波变换 融合规则 效果评价 image fusion wavelet transform fusion rule quality evaluation
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参考文献16

  • 1MatsopouiosGK,MarshallS,BruntJ.Multi-resolution morphological fusion of MR and CT images of the human brain[J].IEEE Transactions on Image and Signal Processing,1994,141(3):137-142.
  • 2Wang Anna,Sun Haijing,Guan Yueyang.The application of wavelet transform to multi-modality medical image fusion//Proceedings of IEEE international Conference on Networking,Sensing and Control.Chicago:IEEE,2006:270-274.
  • 3张泾周,李婷,吴疆.医学图像的小波变换融合算法研究[J].中国生物医学工程学报,2008,27(4):521-525. 被引量:7
  • 4Mallat S.G.,A theory for multiresolution signal decomposition:the wavelet representation[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
  • 5Mallat S G,Zhong S.Characterization of signal from mutiscale edges[J].IEEE Trans on Pattern Analysis and Machine Intelligence,1992,14(7):710-732.
  • 6Jian Muwei,Dong Junyu,Zhang Yang.Image fusion based on wavelet transform//Proceedings of the 8th ACIS International Conference on Software Engineering,Artificial Intelligence,Networking,and Parallel /Distributed Computing.Chicago:IEEE,2007:713-718.
  • 7胡俊峰,唐鹤云,钱建生.不同图像格式在医学图像融合中的性能研究[J].中国生物医学工程学报,2010,29(2):310-313. 被引量:10
  • 8Li H,Manjunath B S,Mitra S K.Multisensor image fusion using the wavelet transform[J].Graphical Models and Image Processing,1995,27(3):235-244.
  • 9刘贵喜,杨万海.基于小波分解的图像融合方法及性能评价[J].自动化学报,2002,28(6):927-934. 被引量:137
  • 10Zhang Z,BlumR.S.ACategorizationof multiscaledecomposition-based image fusion schemes with a performance study for a digital camera application[J].Proceedings of the IEEE,1999,87(8):1315-1326.

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