Serendipity
b8a8ff2bc6
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
306 lines
11 KiB
Matlab
306 lines
11 KiB
Matlab
%% 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)
|
|
title('原始图像')
|
|
|
|
%% 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)
|
|
figure,imshow(z),title('灰度图像')
|
|
|
|
%% 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,:)
|
|
%%
|
|
%% 从此向上，确定网格位置
|
|
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);
|