finding the distance between points of boundaries

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Hi all,
I need help to find the distance between boundaries . I have attached my original microscopy image and the code I wrote to get the more clear boudaries of desired regions of that image . After that my problem is to find the distance between right most points of innner(green) and outer(Red) boundaries . Actually, I have to find the the distance between A and B points indicated in the resulting png.
It would be great if you share some ideas about writing codes for this measurement.
Thank you

Accepted Answer

Image Analyst
Image Analyst on 26 Jul 2020
Babu, to process many images, see the code samples in the FAQ:
To find the distance on the right between the two circles, try this.
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 = 22;
folder = pwd;
baseFileName = 'originalimage.png';
% Get the full filename, with path prepended.
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);
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.
% Use weighted sum of ALL channels to create a gray scale image.
grayImage = rgb2gray(grayImage);
% ALTERNATE METHOD: Convert it to gray scale by taking only the green channel,
% which in a typical snapshot will be the least noisy channel.
% grayImage = grayImage(:, :, 2); % Take green channel.
% Display the image.
subplot(2, 2, 1);
imshow(grayImage, []);
title('Original Grayscale Image', 'FontSize', fontSize, 'Interpreter', 'None');
hFig = gcf;
hFig.WindowState = 'maximized'; % May not work in earlier versions of MATLAB.
% Display histogram
subplot(2, 2, 2);
grid on;
title('Histogram of gray image', 'FontSize', fontSize);
% Get a binary image
brightMask = imbinarize(grayImage);
% Take the 2 biggest blobs
brightMask = bwareafilt(brightMask, 1, 4);
% Get rid of the small dots in the star by doing a hole fill.
% binaryImage = imfill(binaryImage, 'holes');
subplot(2, 2, 3);
imshow(brightMask, []);
title('Initial Binary Image', 'FontSize', fontSize, 'Interpreter', 'None');
% Need to get rid of little black specks in blob.
props = regionprops(brightMask, 'Area');
brightArea = props.Area
minAllowableArea = brightArea * 0.9
brightMask = ~bwareafilt(~brightMask, 2);
% Blur it a bit to smooth it out.
windowSize = 17;
kernel = ones(windowSize, windowSize) / windowSize ^ 2;
brightMask = imfilter(brightMask, kernel) > 0.5;
subplot(2, 2, 4);
imshow(brightMask, []);
title('Final Binary Image', 'FontSize', fontSize, 'Interpreter', 'None');
% Get boundaries and plot them, just for fun.
boundaries = bwboundaries(brightMask);
subplot(2, 2, 1);
hold on;
for k = 1 : length(boundaries)
thisBoundary = boundaries{k};
x = thisBoundary(:, 2);
y = thisBoundary(:, 1);
plot(x, y, 'r-', 'LineWidth', 2);
% Find the right-most point.
[r, c] = find(brightMask);
[lastColumn, outerIndex] = max(c)
xRight = c(outerIndex)
yRight = r(outerIndex)
innerBoundary = boundaries{2};
x = innerBoundary(:, 2);
y = innerBoundary(:, 1);
% Find the closest point on the inner boundary to that point.
distances = sqrt((x - xRight).^2 + (y - yRight).^2);
[minDistance, innerIndex] = min(distances)
xInner = innerBoundary(innerIndex, 2);
yInner = innerBoundary(innerIndex, 1);
% Draw a line between them
line([xInner, xRight], [yInner, yRight], 'Color', 'r', 'LineWidth', 2);
subplot(2, 2, 4);
line([xInner, xRight], [yInner, yRight], 'Color', 'r', 'LineWidth', 2);
caption = sprintf('Final Mask. Distance = %.1f', minDistance);
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
Babu Sankhi
Babu Sankhi on 5 Aug 2020
It gives the error;
'export_fig(fullFileNam)' not found. when I use this;
For i=1:100
My code is here for everything to get the figures
pngFileName = sprintf('plot_subject_%d.png', i);
fullFileNam = fullfile('F:\2020\microscope\setup\figur', pngFileName);
% Then save it
which -all export_fig(fullFileNam);
%addpath('F:\2020\microscope\setup\figur', '-end')
I don't get any error when I use this. However no figures are saved. Can you please help me more?
all export_fig(fullFileNam);

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More Answers (1)

Matt J
Matt J on 26 Jul 2020
If you have the Statistics Toolbox, you can pdist2
D = pdist2(Red,Green)
Matt J
Matt J on 26 Jul 2020
But I have thousands of images to do that so using tool box for each is quiet time consuming/and allmost impossible.
The boundarycalculation.m routine that you attached takes about 0.02 sec on my machine once you remove all of the plotting commands. That should allow you to do thousands of images in about a minute or so - doesn't seem so bad to me.
Using the alternative version below, I was able to cut the time by about half. The imclose operations that I commented out may or may not be helpful in removing noise, and in any case don't add much overhead:
BW=bwareafilt(imbinarize (II),1);
%Elapsed time is 0.009772 seconds.

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