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An Algorithm of Medical Image Fusion Based on Shearlet Transformation |
Software Department, Chengdu University of Information Technology, Chengdu 610225, China |
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Abstract Shearlet transform is a novel multiscale geometric analysis tool that has many virtue such as multiresolution, multidirectional, high efficiency and has unique advantages compared with the wavelet transform, curvelet transform and contourlet transform. This paper proposed a novel fusion method for medical image based on shearlet transform. First, two original images were decomposed into different frequency sub-band coefficients by using shearlet. Next, the selection of the lowfrequency sub-band coefficient and the high-frequency directional sub-band coefficient were discussed. The method based on non-negative matrix factorization(NMF)was used to fuse the low-frequency sub-band coefficient, and for the high-frequency directional sub-band coefficient, this paper proposed a maximum visual energy contrast method that chose coefficient based on the local contrast and the sum of local regional energy after studying the human visual characteristics closely. At last, the fused image was obtained by performing the inverse shearlet on the combined coefficients. The proposed fusion method was compared with the other three fusion methods in two sets of experiments, and four of the five objective evaluation indicators also have reached the optimal value. In conclusion, the proposed fusion method has a considerable improvement in subjective fusion quality and objective evaluation
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[1]陈晓艳, 李健楠, 王化祥. 一种电阻抗图像与CT 图像融合方法研究[J].中国生物医学工程学报,2012,30(6): 892-896.
[2]Constantinos S, Pattichis MS, MicheliTzanakou E. Medical imaging fusion applications: An overview[C] //Conference Record of the ThirtyFifth Asilomar Conference on Signals, Systems and Computers. Pacific Grove: IEEE,2001:1263-1267.
[3]Das S, Kundu MK. NSCTbased multimodal medical image fusion using pulsecoupled neural network and modified spatial frequency [J]. Medical and Biological Engineering and Computing, 2012, 50(10): 1105-1114.
[4]Li Shutao, Yin Haitao, Fang Leyuan. Groupsparse representation with dictionary learning for medical image denoising and fusion[J]. IEEE Transactions on Biomedical Engineering, 2012,59(12): 3450 - 3459.
[5]Singh R, Srivastava R, Prakash O, et al. Multimodal medical image fusion in dual tree complex wavelet transform domain using maximum and average fusion rules[J]. Journal of Medical Imaging and Health Informatics, 2012,2(2): 168-173.
[6]Townsend DW, Beyer T. A combined PET/CT scanner: the path to true image fusion[J]. British journal of Radiology, 2002, 75(9): S24-S30.
[7]Yang Liu, Guo Baolong, Ni Wei. Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform[J].Neurocomputing, 2008, 72(1): 203-211.
[8]杨立才, 刘延梅, 刘欣, 等. 基于小波包变换的医学图像融合方法[J]. 中国生物医学工程学报, 2009, 28(1): 12-16.
[9]Guo Kanghui, Labate D. Optimally sparse multidimensional representation using shearlets[J]. SIAM journal on mathematical analysis, 2008, 39(1): 298-318.
[10]Kutyniok G, Labate D. Resolution of the wavefront set using continuous shearlets[J]. Trans Amer Math Soc, 2009, 361(5): 2719-2754.
[11]Easley G, Labate D, Lim WQ. Sparse directional image representations using the discrete shearlet transform[J]. Applied and Computational Harmonic Analysis, 2008, 25(1): 25-46.
[12]Candes EJ, Donoho DL. Curvelets: A surprisingly effective nonadaptive representation for objects with edges[C]//in Curve and Surface Fitting: SaintMalo, Nashville,USA: Vanderbilt University Press,2000:105-120.
[13]Do MN, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing, 2005 , 14(12): 2091-2106.
[14]Cunha AL, Zhou J, Do MN. The nonsubsampled contourlet transform: theory, design, and applications[J]. IEEE Transactions on Image Processing, 2006 , 15(10): 3089-3101.[15]Miao Qiguang, Shi Cheng, Xua Pengfei, et al. A novel algorithm of imagefusion using shearlets[J]. Optics Communications,2011,284(6): 1540-1547.
[16]郑红,郑晨,闫秀生,陈海霞. 基于剪切波变换的可见光与红外图像融合算法[J]. 仪器仪表学报,2012,33(7):1613-1619.
[17]Lee DD, Seung HS. Learning the parts of objects by nonnegative matrix factorization[J]. Nature, 1999, 401(6755): 788-791.
[18]Lim WQ. The discrete shearlet transform: A new directional transform and compactly supported shearlet frames[J]. IEEE Transactions on Image Processing, 2010 , 19(5): 1166-1180.
[19]Seung D, Lee L. Algorithms for nonnegative matrix factorization[J]. Advances in Neural Information Processing Systems, 2001, 13(2): 556-562.
[20]苗启广,王宝树. 基于非负矩阵分解的多聚焦图像融合研究[J]. 光学学报, 2005, 25(6): 755-759.
[21]苗启广, 王宝树. 图像融合的非负矩阵分解算法[J]. 计算机辅助设计与图形学学报, 2005,17(7): 2029-2032.
[22]Legge GE, Foley JM. Contrast masking in human vision[J]. JOSA, 1980,70(12): 1458-1471.
[23]Watson AB. DCT quantization matrices visually optimized for individual images[C]// Human Vision, Visual Processing and Digital Display IV. San Jose: SPIE, 1993: 202-216.[24]Watson AB. Efficiency of a model human image code[J]. JOSA A, 1987,4(12): 2401-2417.
[25]Legge GE. A power law for contrast discrimination[J]. Vision Research, 1981, 21(4): 457-467.
[26]http://www.bic.mni.mcgill.ca/brainweb/. |
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