基于3-D剪切波和广义高斯模型的多模态医学序列图像融合
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摘要
鉴于大多数传统的多模态医学图像融合算法面临无法处理医学序列图像的局限性,提出了一种基于3-D剪切波(3DST)和广义高斯模型的多模态医学序列图像融合方法。首先,通过3-D剪切波变换获得序列图像的低频部分和高频部分;其次,低频部分采用一种新的基于局部能量的融合方法;然后,高频部分采用基于广义高斯模型(Gene-ralized Gaussian Model,GGD)和模糊逻辑的融合方法;最后,通过3-D剪切波的逆变换获得融合的医学序列图像。通过实验对融合图像的主客观性能进行比较,结果表明所提算法获得了更好的融合效果。
In view of the limitation of most traditional multi-modal medical image fusion methods that cannot deal with the medical volumetric images,this paper presented a multi-modal medical volumetric image fusion method based on 3-D shearlet transform(3 DST) and generalized gaussian model.Firstly,the preregistered medical volumetric images are decomposed into low frequency parts and high frequency parts by using the 3 DST.Next,a novel fusion rule with the local energy is performed on the low frequency subbands.Moreover,an effective fusion rule based on Generalized Gaussian Model(GGD) and fuzzy logic is proposed for integrating the high frequency subbands.Finally,the fused image is obtained by the inverse 3 DST.Through subjective and objective performance comparison,experiments on medical volumetric images show that the proposed method can obtain better fusion results.
In view of the limitation of most traditional multi-modal medical image fusion methods that cannot deal with the medical volumetric images,this paper presented a multi-modal medical volumetric image fusion method based on 3-D shearlet transform(3 DST) and generalized gaussian model.Firstly,the preregistered medical volumetric images are decomposed into low frequency parts and high frequency parts by using the 3 DST.Next,a novel fusion rule with the local energy is performed on the low frequency subbands.Moreover,an effective fusion rule based on Generalized Gaussian Model(GGD) and fuzzy logic is proposed for integrating the high frequency subbands.Finally,the fused image is obtained by the inverse 3 DST.Through subjective and objective performance comparison,experiments on medical volumetric images show that the proposed method can obtain better fusion results.
引文
[1] JAMES A P,DASARATHY B V.Medical image fusion:A su- rvey of the state of the art[J].Information Fusion,2014,19(3):4-19.
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[14] MARCHI V A A,ROJAS F A R,LOUZADA F.The chi-plot and its asymptotic confidence interval for analyzing bivariate dependence:an application to the average intelligence and atheism rates across nations data[J].Journal of Data Science,2012,10(4):711-722.
[15] MOULIN P,LIU J.Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors[J].IEEE Transactions on Information Theory,1999,45(3):909-919.
[16] GAI S,YANG G,WAN M,et al.Hidden Markov tree model of images using quaternion wavelet transform[J].Computers & Electrical Engineering,2014,40(3):819-832.
[17] WANG L,LI B,TIAN L F.EGGDD:An explicit dependency model for multi-modal medical image fusion in shift-invariant shearlet transform domain[J].Information Fusion,2014,19(11):29-37.
[18] JIANG Q,JIN X,LEE S J,et al.A Novel Multi-focus Image Fusion Method Based on Stationary Wavelet Transform and Local Features of Fuzzy Sets[J].IEEE Access,2017,5:20286-20302.
[19] LI S,KANG X,HU J.Image fusion with guided filtering[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2013,22(7):2864-2875.
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1)www.med.harvard.edu/aanlib
1)www.med.harvard.edu/aanlib.
[2] GALANDE A,PATIL R.The art of medical image fusion:A survey[C]//International Conference on Advances in Computing,Communications and Informatics.IEEE,2013:400-405.
[3] SAHU A,BHATEJA V,KRISHN A,et al.Medical image fusion with Laplacian Pyramids[C]//International Conference on Medical Imaging,M-Health and Emerging Communication Systems.IEEE,2015:448-453.
[4] HARIBABU M,BINDU C H,PRASAD K S.Multimodal Medical Image Fusion of MRI-PET Using Wavelet Transform[C]//International Conference on Advances in Mobile Network,Communication and ITS Applications.IEEE,2012:127-130.
[5] ZHANG Q,GUO B L.Multifocus image fusion using the nonsubsampledcontourlettransform[J].Signal Processing,2009,89(7):1334-1346.
[6] RUI S,IRENE C,ANUP B.Cross-scale coefficient selection for volumetric medical image fusion[J].IEEE Transactions on Biomedical Engineering,2013,60(4):1069-1079.
[7] WANG L,LI B,TIAN L.Multimodal medical volumetric data fusion using 3-D discrete shearlet transform and global-to-local rule.[J].IEEE Transactions on Biomedical Engineering,2013,61(1):197-206.
[8] LIU Y,CHEN X,CHENG J,et al.A medical image fusion method based on convolutional neural networks[C]//International Conference on Information Fusion.IEEE,2017:1-7.
[9] HERMESSI H,MOURALI O,ZAGROUBA E.Convolutional neural network-based multimodal image fusion via similarity learning in the shearletdomain[J].Neural Computing & Applications,2018(4):1-17.
[10] CROUSE M S,BARANIUK R G.Contextual hidden Markov models for wavelet-domain signal processing[C]//Asilomar Conference on.IEEE,1997:95-100.
[11] PO D D,DO M N.Directional multiscale modeling of images using the contourlettransform[J].IEEE Transactions on Image Processing,2006,15(6):1610-1620.
[12] ZHU M,YANG Y.A New Image Fusion Algorithm Based on Fuzzy Logic[C]//International Conference on Intelligent Computation Technology and Automation.IEEE Computer Society,2008:83-86.
[13] CHEN S,WU Y.Image Fusion Based on Contourlet Transform and Fuzzy Logic[C]//International Congress on Image and Signal Processing.IEEE,2009:1-5.
[14] MARCHI V A A,ROJAS F A R,LOUZADA F.The chi-plot and its asymptotic confidence interval for analyzing bivariate dependence:an application to the average intelligence and atheism rates across nations data[J].Journal of Data Science,2012,10(4):711-722.
[15] MOULIN P,LIU J.Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors[J].IEEE Transactions on Information Theory,1999,45(3):909-919.
[16] GAI S,YANG G,WAN M,et al.Hidden Markov tree model of images using quaternion wavelet transform[J].Computers & Electrical Engineering,2014,40(3):819-832.
[17] WANG L,LI B,TIAN L F.EGGDD:An explicit dependency model for multi-modal medical image fusion in shift-invariant shearlet transform domain[J].Information Fusion,2014,19(11):29-37.
[18] JIANG Q,JIN X,LEE S J,et al.A Novel Multi-focus Image Fusion Method Based on Stationary Wavelet Transform and Local Features of Fuzzy Sets[J].IEEE Access,2017,5:20286-20302.
[19] LI S,KANG X,HU J.Image fusion with guided filtering[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2013,22(7):2864-2875.
[20] BHATNAGAR G,WU Q M J,LIU Z.Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain[J].International Journal of Engineering Trends & Technology,2013,15(5):1014-1024.
1)www.med.harvard.edu/aanlib
1)www.med.harvard.edu/aanlib.