【图像融合】基于耦合特征学习的多模式医学图像融合附matlab代码
【图像融合】基于耦合特征学习的多模式医学图像融合附matlab代码
TT_Matlab
博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，完整matlab代码或者程序定制加qq1575304183。
1 简介
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 部分代码
%
%% 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 vectors
opts.rho = 10; % optimization penalty term
opts.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); % Fusion
toc; % runtime
%
% results
F = uint8(IF*255);
imwrite(F,[’Results’ fusion_mods ’_F.png’]);
figure(23)
subplot 131
imshow(I1rgb,[])
xlabel(’I_1’)
subplot 132
imshow(I2,[])
xlabel(’I_2’)
subplot 133
imshow(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 仿真结果
4 参考文献
[1] Veshki F G , Ouzir N , Vorobyov S A , et al. Coupled Feature Learning for Multimodal Medical Image Fusion[J]. 2021.
博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，相关matlab代码问题可私信交流。
部分理论引用网络文献，若有侵权联系博主删除。