cDNA-image-processing/参考资料/NewGridAndCV/GriddingAndCV.m

%% Microarray Spot Finding Example
% This example shows a simple method for locating spots on a microarray and
% extracting the intensties of the spots. It can be downloaded from *MATLAB
% Central*.
% http://www.mathworks.com/matlabcentral
%
% Copyright 2004-2010 RBemis The MathWorks, Inc. 

%% Start with clean slate
clear           %empty workspace (no variables)
close all       %no figures
clc             %empty command window

%% Read image file
% MATLAB can read many standard image formats including TIFF, GIF and BMP
% using the |imread| command. In addition, the *Image Procesing Toolbox*
% provides support for working with specialized image file formats such as
% DICOM. This microarray image was stored as a J-PEG file. The image is
% much larger than the screen size, so |imshow| scales it down to fit and
% let's you know with a warning message.
% x = imread('MicroArraySlide.JPG');
% imageSize = size(x)
% screenSize = get(0,'ScreenSize')
% 
% iptsetpref('ImshowBorder','tight')
% imshow(x)
% title('original image')

%% Crop specified region
% Next we use |imcrop| to extract a region of interest. You can repeat this
% for all print-tip blocks for a full microarray study.
% y = imcrop(x,[622 2467 220 227]);


y=imread('test.tif');
% y=imread('cDNA.png');
% y=imread('test001.tif');
% y = imread('C:\Users\Administrator\Desktop\中国生物医学工程学报（实验结果对比）\实验二\test001.tif');
% y = imread('C:\Users\Administrator\Desktop\中国生物医学工程学报（实验结果对比）\实验三\test003.tif');
f1 = figure('position',[40 46 285 280]);
imshow(y)

%% Display red & green layers
% This image was stored in RGB format.  We are only interested in the red
% and green planes. To extract the red plane, simply index layer 1. For the
% green plane, layer 2. Custom colormaps make visualization more intuitive.
% Notice that spot shapes are not necessarily the same in both colors.
f2 = figure('position',[265 163 647 327]);
subplot(121)
redMap = gray(256);
redMap(:,[2 3]) = 0;
subimage(y(:,:,1),redMap)
axis off
title('red (layer 1)')
subplot(122)
greenMap = gray(256);
greenMap(:,[1 3]) = 0;
subimage(y(:,:,2),greenMap)
axis off
title('green (layer 2)')

%% Convert RGB image to grayscale for spot finding
% Initially we care more about where the spots are located than their red
% and green intensities. Converting from RGB color to grayscale allows us
% to focus first on spot locations.
z = rgb2gray(y);
figure(f1)
imshow(z)

%% Create horizontal profile
% We are looking for a regular grid of spots so we start by looking at the
% mean intensity for each column of the image. This will help us identify
% where the centres of the spots are and where the gaps between the spots
% can be found.
xProfile = mean(z);
f2 = figure('position',[39 346 284 73]);
plot(xProfile)
title('horizontal profile')
axis tight

%% Estimate spot spacing by autocorrelation
% Ideally the spots would be periodicaly spaced consistently printed, but
% in practice they tend to have different sizes and intensities, so the
% horizontal profile is irregular. We can use autocorrelation to enhance
% the self similarity of the profile. The smooth result promotes peak
% finding and estimation of spot spacing. The *Signal Processing Toolbox*
% allows easy computation of the autocorrelation function using the |xcov|
% command.  

ac = xcov(xProfile);                        %unbiased autocorrelation
f3 = figure('position',[-3 427 569 94]);
plot(ac)
s1 = diff(ac([1 1:end]));                   %left slopes
s2 = diff(ac([1:end end]));                 %right slopes
maxima = find(s1>0 & s2<0);                 %peaks
estPeriod = round(median(diff(maxima)))     %nominal spacing
hold on
plot(maxima,ac(maxima),'r^')
hold off
title('autocorrelation of profile')
axis tight

%% Remove background morphologically
% We can use the spacing estimate to help design a filter to remove the
% background noise from the intensity profile. We do this with the
% |imtophat| function from the *Image Processing Toolbox*.  The |strel|
% command creates a simple rectangular 1D window or line shaped structuring
% element.
seLine = strel('line',estPeriod,0);
xProfile2 = imtophat(xProfile,seLine);
f4 = figure('position',[40 443 285 76]);
plot(xProfile2)
title('enhanced horizontal profile')
axis tight

%% Segment peaks
% Now that we have clean and anchored gaps between the peaks, we can number
% each peak region with the |bwlabel| command. These regions were segmented
% by thresholding with |im2bw|. The threshold value was automatically
% determined by statistical properties of the data using |graythresh|. This
% is a good example of image processing techniques are often useful for 1D
% data analysis.
level = graythresh(xProfile2/255)*255
bw = im2bw(xProfile2/255,level/255); 
L = bwlabel(bw);  
f5 = figure('position',[40 540 285 70]);
plot(L)
axis tight
title('labelled regions')

%% Locate centers
% We can extract the centroids of the peaks. These correspond to the
% horizontal centres of the spots. This is a common blob analysis or
% feature extraction task that can be done with |regionprops|.
stats = regionprops(L);
centroids = [stats.Centroid];
xCenters = centroids(1:2:end)
figure(f5)
hold on
plot(xCenters,1:max(L),'ro')
hold off
title('region centers')

%% Determine divisions between spots
% The midpoints between adjacent peaks provides grid point locations.
gap = diff(xCenters)/2;
first = xCenters(1)-gap(1);
xGrid = round([first xCenters(1:end)+gap([1:end end])])
figure(f2)
for i=1:length(xGrid)
  line(xGrid(i)*[1 1],ylim,'color','m')
end
title('vertical separators')

%% Transpose and repeat
% We just did the analysis on the vertical grid. Now we want to do the same
% for the horizontal spacing. To do this, we simply transpose the image and
% repeat all the steps used above. This time without intermediate graphics
% display commands in order to summarize the mathematical steps of this
% algorithm.

yProfile = mean(z');                        %peak profile
ac = xcov(yProfile);                        %cross correlation
p1 = diff(ac([1 1:end]));
p2 = diff(ac([1:end end]));
maxima = find(p1>0 & p2<0);                 %peak locations
estPeriod = round(median(diff(maxima)))     %spacing estimate
seLine = strel('line',estPeriod,0);
yProfile2 = imtophat(yProfile,seLine);      %background removed
level = graythresh(yProfile2/255);          %automatic threshold level
bw = im2bw(yProfile2/255,level);            %binarized peak regions
L = bwlabel(bw);                            %labeled regions
stats = regionprops(L);
centroids = [stats.Centroid];               %centroids
yCenters = centroids(1:2:end)               %Y parts only
gap = diff(yCenters)/2;                     %inner region half widths
first = yCenters(1)-gap(1);

% list defining vertical boundaries between spot regions
yGrid = round([first yCenters(1:end)+gap([1:end end])])

%% Put bounding boxes around each spot
% We have now found the rectangular grid. Using pairs of neighboring grid
% points we can form bounding box regions to address each spot
% individually. The position and size coordinates of each bounding box were
% tabulated for convenience into a 4-column matrix called |ROI|, which
% stands for regions of interest.
% figure(f1)
figure()
imshow(z)
line(xGrid'*[1 1],yGrid([1 end]),'color','b')
line(xGrid([1 end]),yGrid'*[1 1],'color','b')
[X,Y] = meshgrid(xGrid(1:end-1),yGrid(1:end-1));    %xGrid和yGrid中存储了分格子的数据
[dX,dY] = meshgrid(diff(xGrid),diff(yGrid));
ROI = [X(:) Y(:) dX(:) dY(:)];
% first few rows of ROI table
ROI(1:5,:)
%%
%
I=y;
% location_row=xGrid;
% location_col=yGrid;

location_row=yGrid;
location_col=xGrid;

for ii = 1:length(location_row)-1
    for jj = 1: length(location_col)-1
        subimage = I([location_row(ii):location_row(ii+1)],[location_col(jj):location_col(jj+1)],:);%从I中取出每个grid的数据
        subimage_double = double(subimage);
        
        %%%% 转换数据到[0,1]之间，然后转换到[0,255]之间
        sumimage_01 = (subimage_double-min(subimage_double(:)))./max(subimage_double(:))-min(subimage_double(:)); %将每个grid的数据转换到[0,1]之间
%         subimage_norm = uint8(255*sumimage_01);%原文
         subimage_norm = uint8(255*sumimage_01);
        
        %%%% TV去噪 
        subimage_norm(:,:,1)= tvdenoise(double(subimage_norm(:,:,1)),0.01);
        subimage_norm(:,:,2)= tvdenoise(double(subimage_norm(:,:,2)),0.01);
        subimage_norm(:,:,3)= tvdenoise(double(subimage_norm(:,:,3)),0.01);
         
        I_norm([location_row(ii):location_row(ii+1)],[location_col(jj):location_col(jj+1)],:)=subimage_norm;%每个grid的数据
        

        
%%
            %用CV方法进行分割
        
       if mean(mean(subimage_norm(:,:,1)))<5  %%如果均值小于5，增强5图像
          subimage_norm=5*subimage_norm;
         else if mean(mean(subimage_norm(:,:,1)))<30  %%如果均值小于30，增强1.5图像
          subimage_norm=1.5*subimage_norm;
             end
       end
        
        iter=500;
        dt=0.1;
        u0=cvseg(subimage_norm,iter,dt);  %C-V水平集分割
%         u1=im2bw(u0);
          
          %非数字的点设为默认值0
          [m_u0,n_u0]=size(u0);
           for i=1:m_u0
               for j=1:n_u0
                   if(isnan(u0(i,j)))
                       u0(i,j)=0;
                   end
               end
           end
%         subimage_bw=u0;
        %若最后一列的第一个点或最后一个点为白色，则取反  
        u1=u0;
        if(u1(end,1)==1 || u1(end,end))
            u2=1-u1;
        else
            u2=u1;
        end
        subimage_bw=u2;

% subimage_bw为获得的二值图像，即为传递到下一步的图像
        %%
        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%分割结束%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
              
        I_bw([location_row(ii):location_row(ii+1)],[location_col(jj):location_col(jj+1)],:)=subimage_bw;%每个grid的数据;


    end
end
figure,imshow(I_bw)
%显示与原图大学相等的二值图像
all_bw=zeros(size(y(:,:,1)));
all_bw(1:location_row(end),1:location_col(end))=I_bw;
figure(),imshow(all_bw)
%%
I_bw1=all_bw;
I_bw_01=choice(I_bw1,100);   %%剔除面积大于100的目标（100仅为本次设定的阈值）,这里最好用直方图的方法确定阈值，则更具普适性
figure(),imshow(I_bw_01);
%%
num_sub_I_bw_02=0;
for ii = 1:length(location_row)-1
    for jj = 1: length(location_col)-1
        sub_I_bw_01 = I_bw_01([location_row(ii):location_row(ii+1)],[location_col(jj):location_col(jj+1)],:);
        sub_I_bw_02=choosemaxobj(sub_I_bw_01,8);%剔除面积小于20的点
        
        [m,n]=size(sub_I_bw_02);
        for i=1:m
            for j=1:n
                if(sub_I_bw_02(1,j)==1 || sub_I_bw_02(i,1)==1|| sub_I_bw_02(m,j)==1 || sub_I_bw_02(i,n)==1)
                    num_sub_I_bw_02=num_sub_I_bw_02+1;
                end
            end
        end
        
        if(num_sub_I_bw_02>10)
            sub_I_bw_02=zeros(size(sub_I_bw_02));
        end
         
        num_sub_I_bw_02=0;%每次计算完毕清零
        I_bw_Last_01([location_row(ii):location_row(ii+1)],[location_col(jj):location_col(jj+1)],:)=sub_I_bw_02;%每个grid的数据;
    end
end
all_bw_01=zeros(size(y(:,:,1)));
all_bw_01(1:location_row(end),1:location_col(end))=I_bw_Last_01;
figure(),imshow(all_bw_01);