基于matlab对比度和结构提取的多模态解剖图像融合实现基于matlab对比度和结构提取的

一、图像融合简介
二、部分源代码
三、运行结果
四、matlab版本

一、图像融合简介应用多模态图像的配准与融合技术,可以把不同状态的医学图像有机地结合起来,为临床诊断和治疗提供更丰富的信息。介绍了多模态医学图像配准与融合的概念、方法及意义。最后简单介绍了小波变换分析方法。

二、部分源代码

clear; close all; clc; warning off%% A Novel Multi-Modality Anatomical Image FusionMethod Based on Contrast and Structure Extraction% F = fuseImage(I,scale)%Inputs:%I - a mulyi-modal anatomical image sequence%scale - scale factor of dense SIFT, the default value is 16%% load images from the folder that contain multi-modal image to be fused%I=load_images('./Dataset\CT-MRI\Pair 1'); I=load_images('./Dataset\MR-T1-MR-T2\Pair 1'); %I=load_images('./Dataset\MR-Gad-MR-T1\Pair 1'); % Show source input images figure; no_of_images = size(I,4); for i = 1:no_of_imagessubplot(2,1,i); imshow(I(:,:,:,i)); endsuptitle('Source Images'); %%F=fuseImage(I,16); %% Output: F - the fused imageF=rgb2gray(F); figure; imshow(F); function [ F ] = fuseImage(I,scale)addpath('Pyramid_Decomposition'); addpath('Guided_Filter'); addpath('Dense_SIFT'); tic%%[H, W, C, N]=size(I); imgs=im2double(I); IA=zeros(H,W,C,N); for i=1:NIA(:,:,:,i)=enhnc(imgs(:,:,:,i)); end%%imgs_gray=zeros(H,W,N); for i=1:Nimgs_gray(:,:,i)=rgb2gray(IA(:,:,:,i)); end%% %dense sift calculationdsifts=zeros(H,W,32,N, 'single'); for i=1:Nimg=imgs_gray(:,:,i); ext_img=img_extend(img,scale/2-1); [dsifts(:,:,:,i)] = DenseSIFT(ext_img, scale, 1); end%%%local contrastcontrast_map=zeros(H,W,N); for i=1:Ncontrast_map(:,:,i)=sum(dsifts(:,:,:,i),3); end%winner-take-all weighted average strategy for local contrast[x, labels]=max(contrast_map,[],3); clear x; for i=1:Nmono=zeros(H,W); mono(labels==i)=1; contrast_map(:,:,i)=mono; end%% Structure h = [1 -1]; structure_map=zeros(H,W,N); for i=1:Nstructure_map(:,:,i) = abs(conv2(imgs_gray(:,:,i),h,'same')) + abs(conv2(imgs_gray(:,:,i),h','same')); %EQ 13end%winner-take-all weighted average strategy for structure[a, label]=max(structure_map,[],3); clear x; for i=1:Nmonoo=zeros(H,W); monoo(label==i)=1; structure_map(:,:,i)=monoo; end%%weight_map=structure_map.*contrast_map; %weight map refinement using Guided Filterfor i=1:Nweight_map(:,:,i) = fastGF(weight_map(:,:,i),12,0.25,2.5); end% normalizing weight maps%weight_map = weight_map + 10^-25; %avoids division by zeroweight_map = weight_map./repmat(sum(weight_map,3),[1 1 N]); %% Pyramid Decomposition% create empty pyramidpyr = gaussian_pyramid(zeros(H,W,3)); nlev = length(pyr); % multiresolution blendingfor i = 1:N% construct pyramid from each input image% blendfor b = 1:nlevw = repmat(pyrW{b},[1 1 3]); pyr{b} = pyr{b} + w .*pyrI{b}; endend% reconstructF = reconstruct_laplacian_pyramid(pyr); tocend

三、运行结果