如何使用Matlab正确镶嵌图像的细胞? [英] How to properly tesselate a image of cells using matlab?

问题描述

我有以下图片是胰腺细胞的照片

I have the following picture which is a photo of pancreatic cells

我想做的是能够获得每个细胞的膜(红色细丝)，然后进行细分，以了解细丝的长度. 到目前为止，我已经尝试使用matlab网站上给出的示例，但结果并不是很好...

What I would like to do is being able to get the membrane of each cell (red filament) and then do a tessellation in order to get an idea of the length of a filament. So far I have tried to use the example given on the matlab website but the result is not really good...

 I = imread('picture.tiff');
 I_gray = rgb2gray(I);
 [~, threshold] = edge(I_gray, 'sobel');
 fudgeFactor = .5;
 BWs = edge(I_gray,'sobel', threshold * fudgeFactor);
 se90 = strel('line', 3, 90);
 se0 = strel('line', 3, 0);
 BWsdil = imdilate(BWs, [se90 se0]);

我一直在寻找其他方法来做这件事，但是却没有令人满意的结果……有办法吗?也许使用除matlab之外的其他软件可能会更有效率.预先谢谢你！

I have been searching for hours other way to do it but without any satisfying result... Is there a way to do so ? Maybe an other software than matlab could be more efficient. Thank you by advance !

推荐答案

我对细胞或镶嵌或其他任何东西一无所知.但是，如果您想在非统一的背景下检测这些斑点，那么我可能会有所帮助.由于背景不一致，您需要单独分析斑点.您不能只是设置固定的阈值来一次检测所有斑点.首先，您将单独检测每个斑点，然后使用单独的阈值.这是示例

I don't know anything about cells or tessellation or whatever. But if you want to detect those blobs in a non uniform background, then I might help. You need to analyse the blobs individually because of the non uniform background. You can't just set a fixed threshold to detect all blobs at once. First, you will detect each blob individually and then use individual threshold. Here is the example

原始图像

im=imread('gxGkH.jpg');
figure,imagesc(im);axis image;

我只选择蓝色进行分析

imb=im(:,:,3);
figure,imagesc(imb);axis image;

1)模糊图像，因为模糊后斑点会出现局部 中心处的最大值/最小值

1) Blur the image, since after blurring the blobs will have local maxima/minima at their centres

sigma=7;
kernel = fspecial('gaussian',4*sigma+1,sigma);
im2=imfilter(imb,kernel,'symmetric');

figure,imagesc(im2);axis image;

2)使用分水岭变换来分离每个斑点区域

2) Use watershed transform to separate each blob region

% L = watershed(im2);
L = watershed(max(im2(:))-im2);
[x,y]=find(L==0);

绘制边界

figure,imagesc(im2),axis image
hold on, plot(y,x,'r.')

3)在这里，我分别分析每个斑点，并为 每个斑点，然后我检测到斑点并结合所有检测结果

3) Here I analyse each blob individually and find an otsu threshold for each blob, then I detect the blobs and combine all detections

tmp=zeros(size(imb));

for i=1:max(L(:))
  ind=find(L==i);
  mask=L==i;
  [thr,metric] =multithresh(imb(ind),1);
  if metric>0.7
    tmp(ind)=imb(ind)>thr;
  end
end

消除噪音

tmp=imopen(tmp,strel('disk',1));
figure,imagesc(tmp),axis image

如果背景的对比度高于斑点，那么您将不需要在分水岭变换中反转图像.

If the background has higher contrast then the blobs, then you won't need to invert the image in watershed transform.

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