An improved coupled dictionary and multi-norm constraint fusion method for CT/MR medical images

To solve the problems that a single dictionary is difficult to obtain accurate sparse representation of images, and a single norm as activity level measurement of the source image block does not preserve more details of the image, leading to poor image fusion results, this paper proposes an improved coupled dictionary and multi-norm constraint image fusion method for CT/MR images. In the paper, CT and MR image pairs are used as training set, and the coupled CT dictionary and the MR dictionary are obtained by using the improved K-SVD algorithm respectively. The fusion dictionary is obtained by combining coupled CT dictionary and MR dictionary with the spatial domain method. First, the registered source images are compiled into the column vectors and the means are removed. The exact sparse representation coefficients are calculated by the CoefROMP algorithm under the fusion dictionary. Then the multi-norm constraint of the sparse representation coefficients is taken as activity level measurement of the source image blocks, and the sparse representation coefficients are fused by the rule of “choosing the maximum”. Finally, the fused images are obtained by reconstruction. The experimental results show that the proposed method in this paper can effectively retain more image details, improve fusion image contrast and clarity, focal prominent, accelerate the running speed of the algorithm and be applied to clinical diagnosis and auxiliary treatment.

[1]  Yvik Swan,et al.  IT Formulae for Gamma Target: Mutual Information and Relative Entropy , 2016, IEEE Transactions on Information Theory.

[2]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[3]  Michael Elad,et al.  Multi-Scale Dictionary Learning Using Wavelets , 2011, IEEE Journal of Selected Topics in Signal Processing.

[4]  Kanmani Madheswari,et al.  Swarm intelligence based optimisation in thermal image fusion using dual tree discrete wavelet transform , 2017 .

[5]  Michael Elad,et al.  Compression of facial images using the K-SVD algorithm , 2008, J. Vis. Commun. Image Represent..

[6]  Michael Elad,et al.  Learning Multiscale Sparse Representations for Image and Video Restoration , 2007, Multiscale Model. Simul..

[7]  Shutao Li,et al.  Multifocus Image Fusion and Restoration With Sparse Representation , 2010, IEEE Transactions on Instrumentation and Measurement.

[8]  José Herskovits,et al.  A feasible directions method for nonsmooth convex optimization , 2011 .

[9]  Isha Mehra,et al.  Wavelet-based image fusion for securing multiple images through asymmetric keys , 2015 .

[10]  Shutao Li,et al.  Simultaneous image fusion and super-resolution using sparse representation , 2013, Inf. Fusion.

[11]  V. Aslantaş,et al.  A new image quality metric for image fusion: The sum of the correlations of differences , 2015 .

[12]  Yi Chai,et al.  A novel dictionary learning approach for multi-modality medical image fusion , 2016, Neurocomputing.

[13]  Ling Shao,et al.  A local descriptor based on Laplacian pyramid coding for action recognition , 2013, Pattern Recognit. Lett..

[14]  Thomas S. Huang,et al.  Coupled Dictionary Training for Image Super-Resolution , 2012, IEEE Transactions on Image Processing.

[15]  Lian Qiu,et al.  Research Advances on Dictionary Learning Models, Algorithms and Applications , 2015 .

[16]  José Miguel Angulo,et al.  Multifractal Dimensional Dependence Assessment Based on Tsallis Mutual Information , 2015, Entropy.

[17]  Jianren Wang,et al.  Research on the natural image super-resolution reconstruction algorithm based on compressive perception theory and deep learning model , 2016, Neurocomputing.

[18]  Lu Xing,et al.  A multi-scale contrast-based image quality assessment model for multi-exposure image fusion , 2018, Signal Process..

[19]  Weitong Li,et al.  A composite objective metric and its application to multi-focus image fusion , 2017 .

[20]  Hui Deng,et al.  Objective Image-Quality Assessment for High-Resolution Photospheric Images by Median Filter-Gradient Similarity , 2015, 1701.05300.

[21]  Sajjad Beygi,et al.  Blind structural similarity estimation of digital images using quantized discrete cosine transform coefficients , 2013 .

[22]  Yue Qi,et al.  Infrared and visible image fusion method based on saliency detection in sparse domain , 2017 .

[23]  Nannan Yu,et al.  Image Features Extraction and Fusion Based on Joint Sparse Representation , 2011, IEEE Journal of Selected Topics in Signal Processing.

[24]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[25]  Javier Marcello,et al.  Evaluation of Spatial and Spectral Effectiveness of Pixel-Level Fusion Techniques , 2013, IEEE Geoscience and Remote Sensing Letters.

[26]  Zhenhong Jia,et al.  A novel multi-focus image fusion method using PCNN in nonsubsampled contourlet transform domain , 2015 .

[27]  Jonathan M. Nichols,et al.  Denoising infrared maritime imagery using tailored dictionaries via modified K-SVD algorithm. , 2012, Applied optics.

[28]  Haiying Liu,et al.  Stereo Matching Using the Discrete Wavelet Transform , 2007, Int. J. Wavelets Multiresolution Inf. Process..