feat: cDNA微阵列图像处理作业 - Python实现

实现内容： - 网格划分：投影分析 + 自相关估周期 + 白顶帽去背景 + 质心提取 - 三种阈值分割：人工阈值、Otsu自动阈值、迭代阈值 - TV去噪（Chambolle投影算法） - 后处理：去小连通域 + 保留最大连通域 - 完整可视化：网格叠加、阈值对比、收敛曲线、分割结果参考MATLAB代码：NewGridAndCV/demo_GriddingAndCV.m
2026-05-06 19:41:26 +08:00
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+%% 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);