www.pudn.com > segment_ga.rar > threshold_ksw.asv

%利用最佳直方图熵法（KSW熵法）实现灰度图像阈值分割 
%暂未涉及遗传算法 
 
filename=input('输入预进行阈值分割的灰度图像的文件名及路径：','s'); 
 
disp('指定阈值搜索范围（0～1）：'); 
lowvalue=input('下限值：'); 
highvalue=input('上限值：'); 
 
I=imread(filename); 
 
info=imfinfo(filename); 
maxgrayvalue=info.MaxSampleValue; 
mingrayvalue=info.MinSampleValue; 
graynum=maxgrayvalue-mingrayvalue+1; 
 
hist=imhist(I); 
total=0; 
for i=mingrayvalue:maxgrayvalue 
    total=total+hist(i+1); 
end 
hist1=hist/total; 
 
HT=0; 
for i=mingrayvalue:maxgrayvalue 
    if hist1(i+1)==0 
        temp=0; 
    else 
        temp=-hist1(i+1)*log(hist1(i+1)); 
    end 
    HT=HT+temp; 
end 
 
 
% 
lowvalue1=round(lowvalue*graynum); 
highvalue1=round(highvalue*graynum); 
 
for t=lowvalue1:highvalue1 
     
    Pt=0; 
    for i=1:t 
        Pt=Pt+hist1(i); 
    end 
     
    Ht=0; 
    for i=1:t 
        if hist1(i)==0 
           temp=0; 
        else 
           temp=-hist1(i)*log(hist1(i)); 
        end 
        Ht=Ht+temp; 
    end 
     
    if Pt==0 
         
    H(t)=log(Pt*(1-Pt))+Ht/Pt+(HT-Ht)/(1-Pt); 
     
end 
 
H(mingrayvalue+1:lowvalue1-1)=0; 
H(highvalue1+1:maxgrayvalue+1)=0; 
 
max_shannon=max(H); 
t_opt=find(H==max_shannon); 
 
threshold_opt=t_opt/graynum; 
 
I1=im2bw(I,threshold_opt); 
 
disp('灰度图像阈值分割的效果如图所示：'); 
disp('源图为：Fifure No.1'); 
disp('最佳直方图熵法阈值分割后的图像为：Fifure No.2'); 
disp('Otsu法阈值分割后的图像为：Fifure No.3'); 
 
figure(1); 
imshow(I); 
title('源图'); 
 
figure(2); 
imshow(I1); 
title('最佳直方图熵法阈值分割后的图像'); 
 
level=graythresh(I); 
I2=im2bw(I,level); 
figure(3); 
imshow(I2); 
title('Otsu法阈值分割后的图像');