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MATLAB2013b
1）对原始图像进行延拓，然后对进行极值点提取，也就是局部极值点的选取，包括极大值点和极小值点，要求采用领域点比较法，显示出局部极值点，程序运行出来的结果图要体现出提取出来的局部极值点；
2）曲面拟合采用delaunay三角剖分，插值方法首先选择cubic方法，根据实际运行出来的结果图进行插值方法的选取，以得到清晰的imf图像和残余图像为目标，要求程序运行结果图中显示极大极小包络面，delaunay三角剖分不能仅使用matlab自带的delaunay函数，要有优化的过程，验证程序的视频要能体现delaunay函数的使用以及优化的过程，程序运行结果图中要求体现三角剖分的结果以及上下包络面；过程要求：三角剖分图像、极大包络面图像和极小包络面图像；
3）筛选停止条件，
，在验证程序的时候要体现出这个停止条件，Sd在0.1到0.3之间；
4）对于边界问题的处理，根据实际结果调整镜像延拓的大小。过程要求：延拓之前极大极小值包络面图像和延拓之后极大极小值包络面；
5）通过以上步骤，图像结果包括IMF图像和残余图像，要求输出5层IMF图像和5次残余图像；重点是在分解得到的残余图像上，要求残余图像能够清晰的体现出血管结构；
算法流程图
3.部分核心代码
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clc;clear;close all;warning off;addpath 'func\'% %效果动态的显示% views = 1;
%效果静态的显示views = 0;I0 = imread('Images\2.bmp');[R,C,Kss] = size(I0);
if Kss == 1 I = I0; else I = rgb2gray(I0); end[R,C] = size(I);%上下各做延拓K = 240;
X = func_yantuo(I,1,K);figure(1);subplot(121);imshow(I);title('延拓前');subplot(122);
imshow(X);title('延拓后');%I描述原图象的双精度形式orig = X;orig = double(orig);
x_width = size(X,1); [Rr,Cc] = size(X); xl_max = [];xl_min = [];
%迭代次数Iteration = 5;for iter = 1:Iteration %步骤一，极大值点图像和极小值点图像 SD = 10000;
X = orig; SDs = []; ind = 0; while(SD>0.2) fprintf('SD=%d\n\n',SD);
ind = ind + 1; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %建立极值图象,极大值图象XMAX,极小值图象XMIN
[XMAX,XMIN,xl_max,xl_min] = func_find_max_min(X); if views == 0 & ind == 1 & iter == 1
figure(2); subplot(221);imshow(XMAX(K+1:R+K,K+1:C+K),[]);title('极大值点图像(延拓部分不显示)');
subplot(222);imshow(XMIN(K+1:R+K,K+1:C+K),[]);title('极小值点图像(延拓部分不显示)');
subplot(223);imshow(XMAX,[]);title('极大值点图像'); subplot(224);imshow(XMIN,[]);
title('极小值点图像'); end if views == 1 & ind > 1 figure(2);
subplot(221);imshow(XMAX(K+1:R+K,K+1:C+K),[]);title('极大值点图像(延拓部分不显示)');
subplot(222);imshow(XMIN(K+1:R+K,K+1:C+K),[]);title('极小值点图像(延拓部分不显示)');
subplot(223);imshow(XMAX,[]);title('极大值点图像'); subplot(224);imshow(XMIN,[]);
title('极小值点图像'); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%构造上下包络 %delaunay [r_max,c_max,v_max] = find(XMAX);
[r_min,c_min,v_min] = find(XMIN); tri1=delaunay(c_max,r_max);
tri2=delaunay(c_min,r_min); if views == 0 & ind == 1 & iter == 1
figure(3); subplot(121); triplot(tri1,c_max,r_max);title('极大值点三角剖分');
axis square; subplot(122); triplot(tri2,c_min,r_min);title('极小值点三角剖分');
axis square; end if views == 1 & ind > 0 figure(3);
subplot(121); triplot(tri1,c_max,r_max);title('极大值点三角剖分'); axis square;
subplot(122); triplot(tri2,c_min,r_min);title('极小值点三角剖分'); axis square;
end %极大值包络,极小值包络 %基于delaunay的三角剖分插值算法，非常的耗内存，如果你的电脑
内存OK > 32G，那么可以尝试测试 %另外你要求的那个，只能依靠griddata函数的delaunay法来设计，如果自己编程序，估计内存更
不够 %自己写的函数肯定没matlab自带的效率高 %内存>32G 选择： [zi_max,zi_min] = func_max_min_ev
enlop(xl_max,xl_min,Rr,Cc,1);%delaunay %内存<32G 选择： %[zi_max,zi_min] = func_max_min_evenlop(
xl_max,xl_min,Rr,Cc,2); if views == 0 & ind == 1 & iter == 1 figure(4);
subplot(221);imshow(zi_max(K+1:R+K,K+1:C+K),[]);title('极大值包络(延拓部分不显示)'); subplot(
222);imshow(zi_min(K+1:R+K,K+1:C+K),[]);title('极小值包络(延拓部分不显示)'); subplot(223);ims
how(zi_max,[]);title('极大值包络'); subplot(224);imshow(zi_min,[]);title('极小值包络');
end if views == 1 & ind > 0 figure(4); subplot(221);imshow(zi_max(K+1:R+K,
K+1:C+K),[]);title('极大值包络(延拓部分不显示)'); subplot(222);imshow(zi_min(K+1:R+K,K+1:C+K),[]);
title('极小值包络(延拓部分不显示)'); subplot(223);imshow(zi_max,[]);title('极大值包络');
subplot(224);imshow(zi_min,[]);title('极小值包络'); end %计算SD
zi =(zi_max+zi_min)/2; mi = zi(K+1:R+K,K+1:C+K); h = orig(K+1:R+K,K+1:C+K);
SD = max(abs(mi)^2)/max(h).^2; X = X-zi; SDs= [SDs,SD]; end %%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% imf=X; if iter == 1 SDs2 = SDs; imf1 = imf(K+1:R+K,K+1:C+K); r = orig-imf;
r1 = r(K+1:R+K,K+1:C+K); %重建 Rcon1= func_recontruct(I,imf1); end if iter == 2
imf2 = imf(K+1:R+K,K+1:C+K); r = orig-imf; r2 = r(K+1:R+K,K+1:C+K);
%重建 Rcon2= func_recontruct(I,imf2); end if iter == 3 imf3 = imf(K+1:R+K,K+1:C+K);
r = orig-imf; r3 = r(K+1:R+K,K+1:C+K); %重建 Rcon3= func_recontruct(I,imf2+imf3);
end if iter == 4 imf4 = imf(K+1:R+K,K+1:C+K); r = orig-imf; r4 = r(K+1:R+K,K+1:
C+K); %重建 Rcon4= func_recontruct(I,imf2+imf3+imf4); end if iter == 5
imf5 = imf(K+1:R+K,K+1:C+K); r = orig-imf; r5 = r(K+1:R+K,K+1:C+K); %重建
Rcon5= func_recontruct(I,imf2+imf3+imf4+imf5); end orig=orig-imf; pause(1);endfigure;
semilogy(SDs2,'b-o');xlabel('迭代次数');ylabel('SD值');grid on; figure;subplot(231);imshow(I0,[]);
title('原图');subplot(232);imshow(imf1,[]) ;title('imf1');subplot(233);imshow(imf2,[]); title('imf2');
subplot(234);imshow(imf3,[]); title('imf3');subplot(235);imshow(imf4,[]);title('imf4');subplot(236);
imshow(imf5,[]);title('imf5');figure;subplot(231);imshow(I0,[]);title('原图');subplot(232);imshow(r1,[]);
title('r1');subplot(233);imshow(r2,[]);title('r2');subplot(234);imshow(r3,[]);title('r3');subplot(235);
imshow(r4,[]); title('r4');subplot(236);imshow(r5,[]); title('r5'); figure;subplot(221);imshow(Rcon2,[]);
title('滤波图像1');subplot(222);imshow(Rcon3,[]); title('滤波图像2');subplot(223);imshow(Rcon4,[]);
title('滤波图像3');subplot(224);imshow(Rcon5,[]);title('滤波图像4'); figure;subplot(121);imshow(I0,[]);
title('原始图像');subplot(122);imshow(Rcon5,[]); title('处理后图像');1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.
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