thinning - 副本.rar_Zhang—Suen_image processing_skeletonization_th

% @Author: Linbo<linbo.me> % @Version: 1.0 25/10/2014 % This is the implementation of the % Zhang-Suen Thinning Algorithm for skeletonization. clc,clear % load image data % addpath('./data') % Img_Original = imread('starimage2.jpg'); load image4.mat %%%%%%%%%%加入噪声%%%%%%%%%%%%%% image_1=imnoise(image,'gaussian',0,0.05); image_2=imnoise(image_1,'salt & pepper',0.0001); % imshow(image_2) %%%%%%%%%%加入噪声%%%%%%%%%%%%%% %%%%%%%%%%去除噪声%%%%%%%%%%%%%% image_med=medfilt2(image_2); % imshow(image_med) %%%%%%%%%%去除噪声%%%%%%%%%%%%%% % Convert gray images to binary images using Otsu's method % Otsu_Threshold = graythresh(Img_Original); % BW_Original = not(im2bw(Img_Original,Otsu_Threshold)); % must set object region as 1, background region as 0 %%%%%%%二值化%%%%%%%%%%%%% %%%%%%%迭代法%%%%%%%%%%%%% image_iteration=image_med; %初始化阈值 B=image_med; T=0.5*(double(min(B(:)))+double(max(B(:)))); d=false; %通过迭代求最佳阈值 while~d g=B>=T; Tn=0.5*(mean(B(g))+mean(B(~g))); d=abs(T-Tn)<0.5; T=Tn; end % 根据最佳阈值进行图像分割 level=Tn; for i=1:1024 for j=1:1024 if image_med(i,j)>=level BW_Original(i,j)=1; else BW_Original(i,j)=0; end end end %%%%%%%迭代法%%%%%%%%%%%%% imshow(BW_Original) %%%%%%%二值化%%%%%%%%%%%%% %%%%%%%zhangthinning%%%%%%%%%%%%% changing = 1; [rows, columns] = size(BW_Original); BW_Thinned = BW_Original; BW_Del = ones(rows, columns); while changing % BW_Del = ones(rows, columns); changing = 0; % Setp 1 for i=2:rows-1 for j = 2:columns-1 P = [BW_Thinned(i,j) BW_Thinned(i-1,j) BW_Thinned(i-1,j+1) BW_Thinned(i,j+1) BW_Thinned(i+1,j+1) BW_Thinned(i+1,j) BW_Thinned(i+1,j-1) BW_Thinned(i,j-1) BW_Thinned(i-1,j-1) BW_Thinned(i-1,j)]; % P1, P2, P3, ... , P8, P9, P2 if (BW_Thinned(i,j) == 1 && sum(P(2:end-1))<=6 && sum(P(2:end-1)) >=2 && P(2)*P(4)*P(6)==0 && P(4)*P(6)*P(8)==0) % conditions % No. of 0,1 patterns (transitions from 0 to 1) in the ordered sequence A = 0; for k = 2:size(P,2)-1 if P(k) == 0 && P(k+1)==1 A = A+1; end end if (A==1) BW_Del(i,j)=0; changing = 1; end end end end BW_Thinned = BW_Thinned.*BW_Del; % the deletion must after all the pixels have been visited % Step 2 % for i=2:rows-1 % for j = 2:columns-1 % P = [BW_Thinned(i,j) BW_Thinned(i-1,j) BW_Thinned(i-1,j+1) BW_Thinned(i,j+1) BW_Thinned(i+1,j+1) BW_Thinned(i+1,j) BW_Thinned(i+1,j-1) BW_Thinned(i,j-1) BW_Thinned(i-1,j-1) BW_Thinned(i-1,j)]; % if (BW_Thinned(i,j) == 1 && sum(P(2:end-1))<=6 && sum(P(2:end-1)) >=2 && P(2)*P(4)*P(8)==0 && P(2)*P(6)*P(8)==0) % conditions % A = 0; % for k = 2:size(P,2)-1 % if P(k) == 0 && P(k+1)==1 % A = A+1; % end % end % if (A==1) % BW_Del(i,j)=0; % changing = 1; % end % end % end % end BW_Thinned = BW_Thinned.*BW_Del; end %while %%%%%%%zhangthinning%%%%%%%%%%%%% %%%%%%%%%%变成单像素%%%%%%%%%% % % for i=2:rows-1 % for j = 2:columns-1 % P = [BW_Thinned(i,j) BW_Thinned(i-1,j) BW_Thinned(i-1,j+1) BW_Thinned(i,j+1) BW_Thinned(i+1,j+1) BW_Thinned(i+1,j) BW_Thinned(i+1,j-1) BW_Thinned(i,j-1) BW_Thinned(i-1,j-1) BW_Thinned(i-1,j)]; %%%八邻域 % %%检验是否符合90度模板%%%% % if BW_Thinned(i,j)==1 && BW_Thinned(i-1,j)==1; % end % %%检验是否符合90度模板%%%% % end % end %%%%%%%%%%变成单像素%%%%%%%%%% figure subplot(1,2,1) imshow(BW_Original) subplot(1,2,2) imshow(BW_Thinned)