MATLAB Answers

Measure the spreading length using image analysis.

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mathru
mathru on 21 Jun 2020
Answered: Image Analyst on 23 Jun 2020
In the attached image there are three figures where the spreading length is changing. How can I measure the spreading length of a set of images?

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Answers (2)

Image Analyst
Image Analyst on 23 Jun 2020
Try this:
% 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 = 22;
%--------------------------------------------------------------------------------------------------------
% READ IN IMAGE
folder = pwd;
baseFileName = 'spreading.jpeg';
% 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);
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.
% 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.
end
subplot(2, 2, 1);
imshow(grayImage, []);
impixelinfo;
title('Original Gray Scale Image', 'FontSize', fontSize, 'Interpreter', 'None');
hFig = gcf;
hFig.WindowState = 'maximized'; % May not work in earlier versions of MATLAB.
drawnow;
%--------------------------------------------------------------------------------------------------------
% SEGMENTATION OF IMAGE
% Get a binary image
binaryImage = ~imbinarize(grayImage);
% Extract 3 largest blobs only.
binaryImage = bwareafilt(binaryImage, 3);
subplot(2, 2, 2);
imshow(binaryImage, []);
impixelinfo;
title('Binary Image', 'FontSize', fontSize, 'Interpreter', 'None');
hFig = gcf;
hFig.WindowState = 'maximized'; % May not work in earlier versions of MATLAB.
drawnow;
% Get the bounding boxes
props = regionprops(binaryImage, 'BoundingBox');
allBB = vertcat(props.BoundingBox)
% Find out which is on top.
y = allBB(:, 2)
[sortedY, sortOrder] = sort(y, 'ascend')
% Sort props the same way.
allBB = allBB(sortOrder, :)
allWidths = allBB(:, 3)
allHeights = allBB(:, 4)
subplot(2, 2, 3);
bar(allWidths);
grid on;
title('Blob Widths', 'FontSize', fontSize, 'Interpreter', 'None');
xlabel('Blob Number', 'FontSize', fontSize, 'Interpreter', 'None');
ylabel('Width', 'FontSize', fontSize, 'Interpreter', 'None');
subplot(2, 2, 4);
bar(allHeights);
grid on;
title('Blob Heights', 'FontSize', fontSize, 'Interpreter', 'None');
xlabel('Blob Number', 'FontSize', fontSize, 'Interpreter', 'None');
ylabel('Height', 'FontSize', fontSize, 'Interpreter', 'None');

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KALYAN ACHARJYA
KALYAN ACHARJYA on 21 Jun 2020
Two ways: I am considering the pixel distance length based on spaial coordinates (Not the general scale length like mm or cm)
Option 1: Image Segmenattion
  1. Do Image segmentation and find the ROI (Dark part of the images)
  2. Please do some sort of morpho operations to enure having single blob only (Dark region)
  3. Find the distance between farthermost two black pixels
Oprion 2: Using ginput
data=imread('image9.jpeg');
imshow(data);
[x y]=ginput(6);
x=round(x);
y=round(y);
image1_dist=sqrt((x(2)^2-x(1)^2)+y(2)^2-y(1)^2)
image2_dist=sqrt((x(4)^2-x(3)^2)+y(4)^2-y(3)^2)
image3_dist=sqrt((x(6)^2-x(5)^2)+y(6)^2-y(5)^2)
Command Window:
image1_dist =
300.3681
image2_dist =
331.2990
image3_dist =
337.3603
>>

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KALYAN ACHARJYA
KALYAN ACHARJYA on 21 Jun 2020
As images number are quite high, then it must be automated to get the distance, see option 1
mathru
mathru on 21 Jun 2020
It would be easy for me if you can give me a demonstration of that.

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