使用水平集算法进行图像分割的实践与解析

下面是用水平集算法的图像分割程序，哪位高手帮忙大概解释一下 特别是图像显示的那一块（红色字体的是在调用evolution.m）急用～～

Img = imread('t11.jpg');                          % The same cell image in the paper is used here
Img=double(Img(:,:,1));
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=5;                                            % time step, try timestep=10, 20, ..., 50, ...
mu=0.2/timestep;                                     % coefficient of the internal (penalizing) energy term P(\phi)
                                                                % Note: the product timestep*mu must be less than 0.25 for stability!
lambda=5;                                               % coefficient of the weighted length term Lg(\phi)
alf=1.5;                                                   % coefficient of the weighted area term Ag(\phi);
                                                                % Note: Choose a positive(negative) alf if the initial contour is outside(inside) the object.
           
[nrow, ncol]=size(Img);
figure;imagesc(Img, [0, 255]);colormap(gray);hold on;
text(6,6,'Left click to get points, right click to get end point','FontSize',[12],'Color', 'r');
                                                            % Click mouse to specify initial contour/region
BW = roipoly;                                        % get a region R inside a polygon, BW is a binary image with 1 and 0 inside or outside the polygon;
c0=4;                                                      % the constant value used to define binary level set function;
initialLSF= c0*2*(0.5-BW);                      % initial level set function: -c0 inside R, c0 outside R;
u=initialLSF;
[c,h] = contour(u,[0 0],'r');
u=initialLSF;
figure;imagesc(Img, [0, 255]);colormap(gray);hold on;
[c,h] = contour(u,[0 0],'r');