基於matlab對比度和結構提取的多模態解剖圖像融合實現
一、圖像融合簡介
應用多模態圖像的配準與融合技術,可以把不同狀態的醫學圖像有機地結合起來,為臨床診斷和治療提供更豐富的信息。介紹瞭多模態醫學圖像配準與融合的概念、方法及意義。最後簡單介紹瞭小波變換分析方法。
二、部分源代碼
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)); end suptitle('Source Images'); %% F=fuseImage(I,16); %% Output: F - the fused image F=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:N IA(:,:,:,i)=enhnc(imgs(:,:,:,i)); end %% imgs_gray=zeros(H,W,N); for i=1:N imgs_gray(:,:,i)=rgb2gray(IA(:,:,:,i)); end % % %dense sift calculation dsifts=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 contrast contrast_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:N structure_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 Filter for 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 zero weight_map = weight_map./repmat(sum(weight_map,3),[1 1 N]); %% Pyramid Decomposition % create empty pyramid pyr = gaussian_pyramid(zeros(H,W,3)); nlev = length(pyr); % multiresolution blending for 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 % reconstruct F = reconstruct_laplacian_pyramid(pyr); toc end
三、運行結果
四、matlab版本
matlab版本
2014a
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