MRI and PET image fusion using structure tensor and dual ripplet-II transform

Medical image fusion aims at preserving salient image features, reducing the redundancy, and increasing the interpretation quality of images in clinical applications e.g. image-guided surgery. The PET image exhibits functional characteristic with low spatial resolution, while the MRI image exhibits brain tissue anatomy with high spatial resolution. Therefore, the image fusion task is carried out to inject the structural and anatomical information of the high-resolution MRI image into the metabolic information of the PET image. This paper firstly introduces the dual ripplet-II transform (DRT) to overcome the shift variance problem caused by the ripplet-II transform. The proposed transform incorporates the dual-tree complex wavelet into the traditional ripplet-II transform. Secondly, the proposed method takes advantage of the structure tensor and DRT to effectively merge the MRI and PET images. To this end, an objective function is proposed which exploits a weighting matrix to preserve more color and spatial information. Visual and statistical analyses show that the proposed method improves the visual quality and increases the quantitative criteria based on mutual information, edge information, spatial frequency, and structural similarity.

[1]  Wenda Zhao,et al.  Gradient entropy metric and p-Laplace diffusion constraint-based algorithm for noisy multispectral image fusion , 2016, Inf. Fusion.

[2]  Hassan Ghassemian,et al.  Fusion of MS and PAN Images Preserving Spectral Quality , 2015, IEEE Geoscience and Remote Sensing Letters.

[3]  Guo Liang.  Image edge detection based on nonsubsampled contourlet transform , 2011 .

[4]  Lei Yang,et al.  Ripplet: A new transform for image processing , 2010, J. Vis. Commun. Image Represent..

[5]  Hassan Ghassemian,et al.  Combining the spectral PCA and spatial PCA fusion methods by an optimal filter , 2016, Inf. Fusion.

[6]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Vinod Kumar,et al.  CT and MR Image Fusion Scheme in Nonsubsampled Contourlet Transform Domain , 2014, Journal of Digital Imaging.

[8]  A. Cormack,et al.  The Radon transform on a family of curves in the plane. II , 1981 .

[9]  Ashish Khare,et al.  Fusion of multimodal medical images using Daubechies complex wavelet transform - A multiresolution approach , 2014, Inf. Fusion.

[10]  Omid Khayat,et al.  Combination of anisotropic diffusion and non-subsampled shearlet transform for image denoising , 2016, J. Intell. Fuzzy Syst..

[11]  Minh N. Do,et al.  The finite ridgelet transform for image representation , 2003, IEEE Trans. Image Process..

[12]  Huiqian Du,et al.  Structure tensor and nonsubsampled shearlet transform based algorithm for CT and MRI image fusion , 2017, Neurocomputing.

[13]  Vinod Kumar,et al.  Feature-Motivated Simplified Adaptive PCNN-Based Medical Image Fusion Algorithm in NSST Domain , 2016, Journal of Digital Imaging.

[14]  Demetrio Labate,et al.  Optimally Sparse Multidimensional Representation Using Shearlets , 2007, SIAM J. Math. Anal..

[15]  Hamid Reza Shahdoosti,et al.  Image denoising in dual contourlet domain using hidden Markov tree models , 2017, Digit. Signal Process..

[16]  Xiongfei Li,et al.  Image fusion based on simultaneous empirical wavelet transform , 2017, Multimedia Tools and Applications.

[17]  Dennis M. Healy,et al.  A new multiresolution generalized directional filter bank design and application in image enhancement , 2008, 2008 15th IEEE International Conference on Image Processing.

[18]  G. Easley,et al.  Sparse directional image representations using the discrete shearlet transform , 2008 .

[19]  Minh N. Do,et al.  The Nonsubsampled Contourlet Transform: Theory, Design, and Applications , 2006, IEEE Transactions on Image Processing.

[20]  Zhiming Cui,et al.  Medical Image Fusion Based on Wavelet Transform and Independent Component Analysis , 2009, 2009 International Joint Conference on Artificial Intelligence.

[21]  Haixu Wang,et al.  Multimodal medical image fusion based on IHS and PCA , 2010 .

[22]  Ya-Xiang Yuan,et al.  A Nonlinear Conjugate Gradient Method with a Strong Global Convergence Property , 1999, SIAM J. Optim..

[23]  Gemma Piella,et al.  Image Fusion for Enhanced Visualization: A Variational Approach , 2009, International Journal of Computer Vision.

[24]  Vladimir Petrovic,et al.  Objective image fusion performance measure , 2000 .

[25]  Te-Ming Tu,et al.  A new look at IHS-like image fusion methods , 2001, Inf. Fusion.

[26]  Ke Lu,et al.  An overview of multi-modal medical image fusion , 2016, Neurocomputing.

[27]  Gong Zhang,et al.  Image fusion method of SAR and infrared image based on Curvelet transform with adaptive weighting , 2015, Multimedia Tools and Applications.

[28]  Lei Wang,et al.  Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shift-invariant shearlet coefficients , 2014, Inf. Fusion.

[29]  D. Donoho,et al.  Translation-Invariant De-Noising , 1995 .

[30]  Fen Qin,et al.  Fusion of Remote Sensing Images Using Improved ICA Mergers Based on Wavelet Decomposition , 2012 .

[31]  Vladimir S. Petrovic,et al.  Gradient-based multiresolution image fusion , 2004, IEEE Transactions on Image Processing.

[32]  Zheng Liu,et al.  A new contrast based multimodal medical image fusion framework , 2015, Neurocomputing.

[33]  Robert D. Nowak,et al.  Wavelet-based statistical signal processing using hidden Markov models , 1998, IEEE Trans. Signal Process..

[34]  J. E. Fowler,et al.  The redundant discrete wavelet transform and additive noise , 2005, IEEE Signal Processing Letters.

[35]  R. Bracewell.  The Fourier transform. , 1989, Scientific American.

[36]  Sabalan Daneshvar,et al.  MRI and PET image fusion by combining IHS and retina-inspired models , 2010, Inf. Fusion.

[37]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[38]  Baltasar Beferull-Lozano,et al.  Directionlets: anisotropic multidirectional representation with separable filtering , 2006, IEEE Transactions on Image Processing.

[39]  Zhou Wang,et al.  Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[40]  Belur V. Dasarathy,et al.  Medical Image Fusion: A survey of the state of the art , 2013, Inf. Fusion.

[41]  Zhiping Xu,et al.  Medical image fusion using multi-level local extrema , 2014, Inf. Fusion.

[42]  Mei Yang,et al.  A novel algorithm of image fusion using shearlets , 2011 .

[43]  Jun Xu,et al.  Ripplet-II transform for feature extraction , 2010, Visual Communications and Image Processing.

[44]  Balázs Kégl,et al.  Image denoising with complex ridgelets , 2007, Pattern Recognit..

[45]  Richard Baraniuk,et al.  The Dual-tree Complex Wavelet Transform , 2007 .

[46]  Xi Chen,et al.  Remote Sensing Images Fusion Algorithm Based on Shearlet Transform , 2009, 2009 International Conference on Environmental Science and Information Application Technology.