基于matlab对比度和结构提取的多模态解剖图像融合怎么实现

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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_images    subplot(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:N    imgs_gray(:,:,i)=rgb2gray(IA(:,:,:,i));end%% %dense sift calculationdsifts=zeros(H,W,32,N, 'single');for i=1:N    img=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:N    contrast_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:N    mono=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 13   end%winner-take-all weighted average strategy for structure[a, label]=max(structure_map,[],3);clear x;for i=1:N    monoo=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:N        weight_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       % blend    for b = 1:nlev        w = repmat(pyrW{b},[1 1 3]);                pyr{b} = pyr{b} + w .*pyrI{b};    end    end% reconstructF = reconstruct_laplacian_pyramid(pyr);tocend

三、运行结果

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