www.pudn.com > _LevelSet.rar > Demo3_initialFromThresh.m

% This Matlab file demomstrates a variational level set method that improves the original method in Li et al's paper 
%    "Level Set Evolution Without Re-initialization: A New Variational Formulation" 
%    in Proceedings of CVPR'05, vol. 1, pp. 430-436. 
% Author: Chunming Li, all rights reserved. 
% E-mail: li_chunming@hotmail.com 
% URL:  http://www.engr.uconn.edu/~cmli/ 
 
clear all; 
close all; 
Img=imread('noisyStar_SNR_20_to_10.bmp'); 
sigma=1.5;    % scale parameter in Gaussian kernel for smoothing. 
G=fspecial('gaussian',15,sigma); 
Img_smooth=conv2(Img,G,'same');  % smooth image by Gaussiin convolution 
[Ix,Iy]=gradient(Img_smooth); 
f=Ix.^2+Iy.^2; 
g=1./(1+f);  % edge indicator function. 
 
epsilon=1.5; % the papramater in the definition of smoothed Dirac function 
 
timestep=1;  % time step 
mu=0.2;  % coefficient of the internal (penalizing) energy term P(\phi) 
          % Note: The product timestep*mu must be less than 0.25 for stable evolution 
 
lambda=5; % coefficient of the weighted length term Lg(\phi) 
alf=0;   % coefficient of the weighted area term Ag(\phi); 
         % Note: Choose a positive(negative) alf if the initial contour is outside(inside) the object. 
 
 
% define initial level set function (LSF) as -c0, c0 at points outside and inside of a region R, respectively. 
[nrow, ncol]=size(Img);   
c0=2;    % The constant value used to define binary level set function as initial LSF; 
         % Using initial LSF with smaller value of c0 usually speed up the evolution. 
 
% Choose an appropriate threshold and the parameter alf.  
threshChoice=1; 
if threshChoice == 1 
    T=110; alf=0;   % choose the mean of all pixel intensities 
elseif threshChoice == 2 
    T=120; alf=-2; % too high threshold --> loss of foreground --> use negative alf to expand the contour  
else 
    T=100; alf=2;  % too low threshold  --> loss of background --> use positive alf to shrink the contour  
end 
% Note: the intensities of this test image are generated as Gaussian randan 
% numbers with mean 120 in the foreground (the star) and 100 in the background and noise 
% standard deviation 10. The above three thresholds are the mean in the 
% background, foreground, and the the average of them. In practice, these 
% quantities are unknown. They can be estimated by some statistical 
% computation, otherwise the user need to adjust the threshold and the 
% parameter alf to get desired result. Note that this method is suitable for noisy bi-modal 
% images (histogram with two peaks). 
 
BW=(Img>T);  % Define initial LSF from thresholding 
initialLSF=2*c0*(0.5-BW); 
 
u=initialLSF; 
 
figure; 
imagesc(u); 
title('Initial level set function'); 
 
figure; 
imagesc(Img, [0, 255]);colormap(gray);hold on; 
[c,h] = contour(u,[0 0],'r');                           
title('Initial contour');                         
 
% start level set evolution 
for n=1:60 
    u=EVOLUTION_LSD(u, g ,lambda, mu, alf, epsilon, timestep, 1);       
    if mod(n,1)==0 
        pause(0.01); 
        imagesc(Img, [0, 255]);colormap(gray);hold on; 
        [c,h] = contour(u,[0 0],'r');  
        iterNum=[num2str(n), ' iterations'];         
        title(iterNum); 
        hold off; 
    end 
end 
imagesc(Img, [0, 255]);colormap(gray);hold on; 
[c,h] = contour(u,[0 0],'r');  
totalIterNum=[num2str(n), ' iterations'];   
title(['Final contour, ', totalIterNum]); 
 
figure; 
mesh(u); 
title('Final level set function');