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A Medical Image Fusion Algorithm Based on Contourlet Transform and T Mixture Models(PDF)


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A Medical Image Fusion Algorithm Based on Contourlet Transform and T Mixture Models
Xu ChunyanSong YuqingLiu ZheBao Xiang
School of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang 212013,China
T distribution mixture modelsContourlet transformimage fusionGIHS
Medical image fusion has become a hot research in the field of medical image processing,among which the most classical method called Gaussian mixture models with Expectation Maximum(EM)fusion,the most classical method may lose the local detail. This paper presents a fusion algorithm which is based on Contourlet transform and T mixture models. Firstly,the RGB color space of source images are converted to the GIHS space through GIHS(Generalized Intensity-Hue-Saturation)transform. Secondly,with the Contourlet transform,the intensity component are decomposed into multi-resolution representations,and then the maximum absolute value of coefficient is applied to fuse the high frequency,EM algorithm is used to estimate the parameters of T mixture models. Lastly,the new intensity is obtained by inverse Contourlet,combining hue and saturation to get final result. Experimental results indicate that the proposed algorithm can obtain the results with more functional,spatial information and obtain a better evaluation than other mainstream algorithms.


[1] JAMES A P,DASARATHY B V. Medical image fusion:a survey of the state of the art[J]. Information fusion,2014,19(3):4-19.
[2]PAJARES G,CRUZ J M. A wavelet based image fusion tutorial[J]. Pattern recognition,2004,37(9):1 855-1 872.
[3]ZHANG Y K,HE S,CHENG Y J. Image fusion algorithm based on contourlet transform[J]. Advanced materials research,2014,1 044/1 045:1 173-1 177.
[4]SHEN Y,REN E,DANG J W,et al. Nonsubsampled contourlet transform based medical image fusion method[J]. Information technology journal,2013,12(4):749-755.
[5]ZHANG Z,BLUM R S. A categorization and study of multiscale decomposition based image fusion schemes with a performance study for a digital camera application[J]. Proceedings of the IEEE,1999,87(8):1 315-1 326.
[6]PIELLA G. A general framework for multiresolution image fusion from pixels to regions[J]. Information fusion,2003,4(4):259-280.
[7]BHATNAGAR G,WU Q M J,LIU Z. A new contrast based multimodal medical image fusion framework[J]. Neurocomputing,2015,157:143-152.
[8]SINGH R,KHARE A. Fusion of multimodal medical images using Daubechies complex wavelet transform:a multiresolution approach[J]. Information fusion,2014,19(3):49-60.
[9]DO M N,VETTERLI M. Contourlets[J]. Studies in computational mathematics,2003,10(3):83-105.
[10]DO M N,VETTERLI M. The contourlet transform:an efficient directional multiresolution image representation[J]. IEEE transactions on image processing,2005,14(12):2 091-2 106.
[11]ZHANG X. CHEN W B. Medical image fusion based on weighted Contourlet transformation coefficients[J]. Journal of image and graphics,2014,19(1):133-140.
[12]CHAN H,LIU Q X. LI H L,et al. Multimodal medical image fusion based on IHS and PCA[J]. Procedia engineering,2010,7(8):280-285.
[13]YANG J,BLUM R S. A statistical signal processing approach to image fusion for concealed weapon detection[C]//Proc of the IEEE International Conference on Image Processing. New York,2002:513-516.
[14]ZRIBI M. Unsupervised Bayesian image segmentation using orthogonal series[J]. Journal of visual communication and image representation,2007,18(6):496-503.
[15]SHOHAM S. Robust clustering by deterministic agglomeration EM of mixtures of multivariate t-distributions[J]. Pattern recognition,2002,35(5):1 127-1 142.
[16]RAFAEL C G,RICHARD E W. 数字图像处理[M]. 2版. 阮秋琦,阮宇智,译. 北京:电子工业出版社,2007:225-245.
[17]TU T M,SU S C,SHYU H C,et al. A new look at IHS-like image fusion methods[J]. Information fusion,2001,2(3):77-186.
[18]郭雷,李晖晖,鲍永生. 图像融合[M]. 北京:电子工业出版社,2008:90-95.
[19]张永生,张云彬,戴晨光. 天基多源遥感信息融合[M]. 北京:科学出版社,2005:115-127.
[20]GUO L,LIU K. Applying NSCT(nonsubsampled contourlet transform)theory to achieving effective image fusion[J]. Journal of northwestern polytechnical university,2009,27:255-259.
[21]XYDEAS C,PETROVIC V. Objective image fusion performance measure[J]. Electronics letters,2000,36(4):308-309.


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