图像分割的阈值算法matlab实现

图像分割的阈值算法matlab实现【OTSU,1DEntropy,2DEntropy】

今天看了几篇论文，实现了一下，没有验证各算法的有效性

也没有进行定量比较

OTSU

% OTSU method

% 2006/9/4

clc;

clear;

%I = imread('E:\test\chinalake.bmp','bmp');

I = imread('E:\test\lena.png','png');

I = double(I);

I = Medianfilter(I); % median filter

h_Tmean = mean(mean(I));

[height,width] = size(I);

Size = height * width; % the size of the image

h_T = sum(sum(I)); % the total gray value of the image

G_min = min(min(I)); % the min gray value of the image

G_max = max(max(I)); % the max gray value of the iamge I_seg = zeros(height,width); % the array to store the segmented image thresh = 0; % the threshold

num1 = 0;

num2 = 0; % count the num of the pixel from the diffrient class

P1 = 0;

P2 = 0; % the probability of the different class

h_T1 = 0;

h_T2 = 0; % the total gray value of different class

h_T1mean = 0;

h_T2mean = 0; % the mean value of the class

max = 0;

for thresh=G_min:G_max % find the best threshold

h_T1 = 0;

h_T2 = 0;

num1 = 0;

for h=1:height

for w=1:width

if I(h,w) <= thresh

num1 = num1 + 1;

h_T1 = h_T1 + I(h,w);

end

end

end

num2 = Size - num1;

h_T2 = h_T - h_T1;

P1 = num1/Size;

P2 = num2/Size;

h_T1mean = h_T1/num1;

h_T2mean = h_T2/num2;

%D = P1*(h_T1mean - h_Tmean)^2 + P2*(h_T2mean - h_Tmean)^2;

D1 = P1*P2*(h_T1mean - h_T2mean)^2; % the tow equation is equal if D1 > max

max = D1;

T_best = thresh; % T record the best thresh

end

end

%%%%%%% Seg the image %%%%%%%%%

for i=1:height

for j=1:width

if I(i,j) > T_best

I_seg(i,j) = 255;

end

end

end

T_best

figure;

imshow(uint8(I_seg));

figure;

imhist(uint8(I));

***************************************************

一维直方图熵阈值算法

% 1D entropy thresholding method

% Pun提出，Kapur对其阈值和熵进行改进

% 两类：object 和background

% P1 = sum(pi) i：1~T

% P2 = sum(pi) i:T+1~255

% HO = ln(P1) + H1/P1;

% HB = ln(P2) + H2/P2;

% H1 = -sum(pi*ln(pi)); i:1~T

% H2 = -sum(pi*ln(pi)); i:T+1~255

% H = HO + HB;

% T_best = argmax(H);

clc;

clear;

%I = imread('E:\test\chinalake.bmp','bmp');

I = imread('E:\test\lena.png','png');

I = double(I);

I = Medianfilter(I); % median filter

[height,width] = size(I);

Size = height * width; % the size of the image

h_T = sum(sum(I)); % the total gray value of the image

G_min = min(min(I)); % the min gray value of the image

G_max = max(max(I)); % the max gray value of the iamge

I_seg = zeros(height,width); % the array to store the segmented image I_hist = zeros(1,256); % the array to store the hist of the image

thresh = 0; % the threshold

num1 = 0;

num2 = 0; % count the num of the pixel from the diffrient class

P1 = 0;

P2 = 0; % the probability of the different class

h_T1 = 0;

h_T2 = 0; % the total gray value of different class

max = 0;