[image fusion] multimodal medical image fusion based on coupled feature learning with matlab code

1 brief introduction

Multimodal image fusion aims to combine relevant information from images acquired with different sensors. In medical imaging, fused images play an essential role in both standard and automated diagnosis. In this paper, we propose a novel multimodal image fusion method based on coupled dictionary learning. The proposed method is general and can be employed for different medical imaging modalities. Unlike many current medical fusion methods, the proposed approach does not suffer from intensity attenuation nor loss of critical information. Specifically, the images to be fused are decomposed into coupled and independent components estimated using sparse representations with identical supports and a Pearson correlation constraint, respectively. An alternating minimization algorithm is designed to solve the resulting optimization problem. The final fusion step uses the max-absolute-value rule. Experiments are conducted using various pairs of multimodal inputs, including real MR-CT and MR-PET images. The resulting performance and execution times show the competitiveness of the proposed method in comparison with state-of-the-art medical image fusion methods.

2 Part of the code

%%% color-greyscale mutimodal image fusion (functional-anatomical)​clear% clc​addpath('utilities');%% fusion problem% fusion_mods = 'T2-PET';% fusion_mods = 'T2-TC';fusion_mods = 'T2-TI';% fusion_mods = 'Gad-PET';%% parameters​opts.k = 5; % maximum nnonzero entries in sparse vectorsopts.rho = 10; % optimization penalty termopts.plot = false; % plot decomposition components%% loading input images​I1rgb = double(imread(['Source_Images\' fusion_mods '_A.png']))/255;I1ycbcr = rgb2ycbcr(I1rgb);I1 = I1ycbcr(:,:,1);I2 = double(imread(['Source_Images\' fusion_mods '_B.png']))/255;if size(I2,3)>1, I2 = rgb2gray(I2); end%% performing decomposition and fusion​n = 32; b = 8;D0 = DCT(n,b);  % initializing the dictionaries with DCT matrices​tic;[~,~,Ie1,Ie2,D1,D2,A1,A2] = perform_Corr_Ind_Decomp(I1,I2,D0,D0,opts); % Decomposition[IF, IF_int] = Fuse_color(Ie2,Ie1,D2,D1,A2,A1,I1ycbcr); % Fusiontoc; % runtime%% resultsF = uint8(IF*255);imwrite(F,['Results\' fusion_mods '_F.png']);​figure(23)subplot 131imshow(I1rgb,[])xlabel('I_1')subplot 132imshow(I2,[])xlabel('I_2')subplot 133imshow(IF,[])xlabel('I^F')%%  dictionary atoms% ID1 = displayPatches(D1);% ID2 = displayPatches(D2);% % figure(37)% subplot 121% imshow(ID1)% xlabel('D1')% subplot 122% imshow(ID2)% xlabel('D2')​​​​​

3 Simulation results

4 reference

[1] Veshki F G ,  Ouzir N ,  Vorobyov S A , et al. Coupled Feature Learning for Multimodal Medical Image Fusion[J].  2021.​

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