最大熵阈值分割

clear all;
I=imread('image');
subplot(121),imshow(I);
title('原始彩色图像');
if length(size(I))==3
I=rgb2gray(I);
end
[X,Y]=size(I);
v_max=max(max(I));
v_min=min(min(I));
T0=(v_max+v_min)/2; %初始分割阈值
h=imhist(I);
grayp=imhist(I)/numel(I); %求图像像素概率
I=double(I);
H0=-sum(grayp(find(grayp(1:end)>0)).*log(grayp(find(grayp(1:end)>0))));
cout=100; %设置迭代次数为100次
while(cout>0)
Tmax=0;
graypd=0;graypb=0;
Hd=0;Hb=0;
T1=T0;
A1=0;A2=0;
B1=0;B2=0;
for i=1:X %计算灰度平均值
for j=1:Y
if(I(i,j)<=T1)
A1=A1+1;
B1=B1+I(i,j);
else
A2=A2+1;
B2=B2+I(i,j);
end
end
end
M1=B1/A1;
M2=B2/A2;
T2=(M1+M2)/2;
TT=round(T2);
graypd=sum(grayp(1:TT)); %计算分割区域G1的概率和
if(graypd==0)
graypd=eps;
end
graypb=1-graypd;
if graypb==0
graypb=eps;
end
Hd=-sum((grayp(find(grayp(1:TT)>0))/graypd).*log((grayp(find(grayp(1:TT)>0))/graypd))); %计算分割后区域G1的信息熵
Hb=-sum(grayp(TT+(find(grayp(TT+1:end)>0)))/graypb.*log(grayp(TT+(find(grayp(TT+1:end)>0)))/graypb)); %计算分割后区域G1的信息熵
H1=Hd+Hb;
cout=cout-1;
if(abs(H0-H1)<0.0001)|(cout==0)
Tmax=T2;
break;
else
T0=T2;
H0=H1;
end
end
Tmax
cout
for i=1:X %根据所求阈值Tmax转换图像
for j=1:Y
if(I(i,j)<=Tmax)
I(i,j)=0;
else
I(i,j)=1;
end
end
end
subplot(122);imshow(I);
title('图像处理分割后的效果');