how can you determine the threshold that covers the functional area of the image in matlab?
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I have an image of a tissue and I want to make a mask of that tissue that is only of the functional area in the image (minus the long black tubular shapes which are blood vessels that I don't want to include in my measurements). How can I make this mask? Here's what i tried:
% Load image
img = imread('tissueimage.tif');
% Convert to grayscale
gray_img = rgb2gray(img);
% Threshold the image to create a binary mask
threshold = 8;
mask = gray_img > threshold;
% Remove small objects
min_size = 1000;
mask = bwareaopen(mask, min_size);
% Fill holes
mask = imfill(mask, 'holes');
% Erode and dilate the mask to remove noise and smooth edges
se = strel('disk', 5);
mask = imerode(mask, se);
mask = imdilate(mask, se);
% Display the original image and the resulting mask
figure;
subplot(1,2,1); imshow(img); title('Original Image');
subplot(1,2,2); imshow(mask); title('Functional Mask');
Here's what I have done so far:
- Preprocess the image
- Segment the tissue (removed background)
here's is where I'm unsure
Remove the blood vessels
i did try thresholding and picking numbers from 0-10 but 0, gets too many of the blood vessels and 10, I'm not sure if its all of the functional area or just the white pixels.
here's how i imagine it should look like (masked image). How can i get this?
Thanks pictures attached.
-L
3 Commenti
Risposta accettata
Image Analyst
il 4 Mar 2023
@Laura just take your existing mask (top image) and invert it
mask = ~mask;
15 Commenti
Image Analyst
il 9 Mar 2023
OK but how would you create the worm mask? Describe your thought process.
Più risposte (1)
Image Analyst
il 11 Mar 2023
@Laura try this:
% Demo by Image Analyst
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
clear; % Erase all existing variables. Or clearvars if you want.
workspace; % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 16;
markerSize = 40;
%--------------------------------------------------------------------------------------------------------
% READ IN TEST IMAGE
folder = [];
baseFileName = 'laura.jpeg';
fullFileName = fullfile(folder, baseFileName);
% Check if file exists.
if ~exist(fullFileName, 'file')
% The file doesn't exist -- didn't find it there in that folder.
% Check the entire search path (other folders) for the file by stripping off the folder.
fullFileNameOnSearchPath = baseFileName; % No path this time.
if ~exist(fullFileNameOnSearchPath, 'file')
% Still didn't find it. Alert user.
errorMessage = sprintf('Error: %s does not exist in the search path folders.', fullFileName);
uiwait(warndlg(errorMessage));
return;
end
end
grayImage = imread(fullFileName);
% Get the dimensions of the image.
% numberOfColorChannels should be = 1 for a gray scale image, and 3 for an RGB color image.
[rows, columns, numberOfColorChannels] = size(grayImage)
if numberOfColorChannels > 1
% It's not really gray scale like we expected - it's color.
fprintf('It is not really gray scale like we expected - it is color\n');
% Extract the blue channel.
grayImage = rgb2gray(grayImage);
end
%--------------------------------------------------------------------------------------------------------
% Display the image.
subplot(3, 2, 1);
imshow(grayImage, []);
impixelinfo;
axis('on', 'image');
title('Original Gray Scale Image', 'FontSize', fontSize, 'Interpreter', 'None');
% Update the dimensions of the image.
% numberOfColorChannels should be = 1 for a gray scale image, and 3 for an RGB color image.
[rows, columns, numberOfColorChannels] = size(grayImage)
% Maximize window.
g = gcf;
g.WindowState = 'maximized';
drawnow;
%--------------------------------------------------------------------------------------------------------
% Get mask
mask = grayImage > 0;
subplot(3, 2, 2);
imshow(mask);
impixelinfo;
axis('on', 'image');
title('Mask Image', 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
%--------------------------------------------------------------------------------------------------------
% Get Euclidean distance transform
edtImage = bwdist(mask);
subplot(3, 2, 3);
imshow(edtImage, []);
impixelinfo;
axis('on', 'image');
title('EDT Image', 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
%--------------------------------------------------------------------------------------------------------
% Threshold the edt image to get the large black regions.
mask2 = edtImage > 8;
% The mask has sharp corners. OPTIONAL: if you want to smooth them out, blur and threshold.
windowWidth = 9;
mask2 = conv2(double(mask2), ones(windowWidth)/windowWidth^2, 'same') > 0.5;
% Looks like maybe we don't want blobs smaller than 1000 pixels. Remove those. (OPTIONAL)
mask2 = bwareaopen(mask2, 1000);
%--------------------------------------------------------------------------------------------------------
% Measure the areas of all the blobs.
props = regionprops(mask2, 'Area')
allAreas = sort([props.Area])
subplot(3, 2, 4);
imshow(mask2, []);
impixelinfo;
axis('on', 'image');
title('Mask Image', 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
%--------------------------------------------------------------------------------------------------------
% Plot the borders of all the blobs in the overlay above the original grayscale image
% using the coordinates returned by bwboundaries().
% bwboundaries() returns a cell array, where each cell contains the row/column coordinates for an object in the image.
subplot(3, 2, 5);
imshow(grayImage); % Optional : show the original image again. Or you can leave the binary image showing if you want.
% Here is where we actually get the boundaries for each blob.
boundaries = bwboundaries(mask2);
% boundaries is a cell array - one cell for each blob.
% In each cell is an N-by-2 list of coordinates in a (row, column) format. Note: NOT (x,y).
% Column 1 is rows, or y. Column 2 is columns, or x.
numberOfBoundaries = size(boundaries, 1); % Count the boundaries so we can use it in our for loop
% Here is where we actually plot the boundaries of each blob in the overlay.
hold on; % Don't let boundaries blow away the displayed image.
for k = 1 : numberOfBoundaries
thisBoundary = boundaries{k}; % Get boundary for this specific blob.
x = thisBoundary(:,2); % Column 2 is the columns, which is x.
y = thisBoundary(:,1); % Column 1 is the rows, which is y.
plot(x, y, 'r-', 'LineWidth', 2); % Plot boundary in red.
end
hold off;
caption = sprintf('%d Outlines, from bwboundaries()', numberOfBoundaries);
fontSize = 15;
title(caption, 'FontSize', fontSize);
axis('on', 'image'); % Make sure image is not artificially stretched because of screen's aspect ratio.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1321035/image.png)
5 Commenti
Image Analyst
il 13 Mar 2023
Because I think you said you want to measure things about the bright blobs. So you can get a mask of those. It's not needed to get the mask of larger dark areas in order to measure the small white blobs.
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