imagematlabcellstesselation

How to properly tesselate a image of cells using matlab?

I have the following picture which is a photo of pancreatic cells enter image description here

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]);

enter image description here

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 !

Solution

  • 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

    The original image

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

    enter image description here

    I am choosing only the blue colour to analyse

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

    enter image description here

    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;

    enter image description here

    2) Use watershed transform to separate each blob region

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

    Draw the boundaries

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

    enter image description here

    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

    Remove some noise

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

    enter image description here

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