how can you determine the threshold that covers the functional area of the image in matlab?

Question

Laura 2023-3-3

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1922730-how-can-you-determine-the-threshold-that-covers-the-functional-area-of-the-image-in-matlab

评论： Image Analyst 2023-3-13

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 个评论
显示 1更早的评论隐藏 1更早的评论

Laura 2023-3-4

@Image Analyst masked image -

original image

Laura 2023-3-4

@Image Analyst

I am trying to select for all the areas excep the connected black areas.

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Image Analyst 2023-3-4

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1922730-how-can-you-determine-the-threshold-that-covers-the-functional-area-of-the-image-in-matlab#answer_1185090

在 MATLAB Online 中打开

@Laura just take your existing mask (top image) and invert it

mask = ~mask;

15 个评论
显示 13更早的评论隐藏 13更早的评论

Laura 2023-3-9

编辑：Laura 2023-3-9

@Image Analyst the worm mask doesn't exist, its just a photoshop picture of the mask i want to create.

I found your response in another forum that was pretty good! This will work, but my issue is highlighting the red areas that i don't want which isn't showing in my image. Here is the code:

% Read image as RGB

I = imread('bwimage.png'); %including the pull path is better

% Convert to binary

BW = imbinarize(rgb2gray(I));

% Get rid of white border

BWConv = bwconvhull(~BW);

BW = BW & BWConv;

% get region centers and major axis, and orientation

stats = regionprops('table',BW,'BoundingBox','MajorAxisLength','MinorAxisLength');

% Compute length/width ratio

stats.LenWdRatio = stats.MajorAxisLength ./ stats.MinorAxisLength;

% Set threshold and determine which objects

% are greater than the threshold.

thresh = 2.0;

stats.isGreater = stats.LenWdRatio > thresh;

% Show bounding box for objects that met the threshold

figure()

h = imshow(BW);

axis on

hold on

% Plot red rectangles around accepted objects

arrayfun(@(i)rectangle('Position',stats.BoundingBox(i,:),'EdgeColor','r'), find(stats.isGreater));

% Plot yellow rectangles around rejected objects

arrayfun(@(i)rectangle('Position',stats.BoundingBox(i,:),'EdgeColor','y'), find(~stats.isGreater));

I attached my image. mY ONLY ISSUES is how to put the big black circles in the rejected squares. bUt nomatter which threshold I pick the circles don't like up. thANKS very much @Image Analyst

Image Analyst 2023-3-9

OK but how would you create the worm mask? Describe your thought process.

请先登录，再进行评论。

Answer 2

Image Analyst 2023-3-11

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1922730-how-can-you-determine-the-threshold-that-covers-the-functional-area-of-the-image-in-matlab#answer_1190725

在 MATLAB Online 中打开

Laura.jpeg

@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.

5 个评论
显示 3更早的评论隐藏 3更早的评论

Laura 2023-3-12

The answer you gave answered me perfectly. Thanks for the link.

Why doesn't it really matter though? Thansk for the clarification. @Image Analyst

Image Analyst 2023-3-13

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.

请先登录，再进行评论。