MATLAB: How to find intensity of a gray image

Image Processing Toolboxintensity of image

how to find intensity of a gray image?

Best Answer

It depends on what you mean by intensity. I know you've been working, and instructing students at your university, long enough in the image processing field that you know how to take a histogram:

% Let's compute and display the histogram.
[pixelCount, grayLevels] = imhist(grayImage);
subplot(2, 2, 2); 
bar(pixelCount);
grid on;
title('Histogram of original image', 'FontSize', fontSize);
xlim([0 grayLevels(end)]); % Scale x axis manually.

and I know that you know how to get the overall mean of the image:

meanGrayLevel = mean2(grayImage);

I know you already know all that. So now we need to have you explain what you mean by "intensity". What you (and most others) may not know is that the units of a standard optical image are NOT intensity. They're lumens-seconds, a form of visible energy. Why? Think of lumens like watts, but just in the visible wavelength. So it's like energy per second. Because you have Lux (lumens per square meter) at the sensor (CCD), and you integrate that over the area (square meters) of a pixel and over time (seconds), then the units are lumen-seconds, kind of like a visible energy. See this for further discussion.

Now, intensity is an SI base unit along with meter, second, ampere, kelvin, mole, and kilogram. Its units are candela. You can not get the luminous intensity of an image - it doesn't make sense. It is a characteristic of the light source, not a surface with light incident on it. Wikipedia goes into a great amount of detail on all these units.

Anyway, we're back to what you mean by intensity. The mean gray level and histogram are too trivial (and you already know those), so please explain exactly what you mean.

Related Solutions

MATLAB: Compare the levels of gray

See this snippet

% Let's compute and display the histogram.
[pixelCount, grayLevels] = imhist(grayImage);
bar(grayLevels, pixelCount);
grid on;
title('Histogram of image', 'FontSize', 22);
xlim([0 255]); % or 65535 if image is uint16
% Compute and display mean
meanGrayLevel = mean2(grayImage);
message = sprintf('The mean gray level is %.2f', meanGrayLevel);
uiwait(helpdlg(message));

MATLAB: Counting the number of muscle fibres in an image

Vishakha:

You might have to take just one color channel, then get rid of the local intensity variations with a bottom hat filter. Then threshold and call bwareafilt() to get rid of blobs bigger or smaller than the known size of the fibers. Then call bwlabel() to count the number of fibers.

Try this for a start - adapt as needed:

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 short g;
format compact;
fontSize = 25;
%===============================================================================

% Get the name of the image the user wants to use.
baseFileName = 'netmuscle.jpg';
% Get the full filename, with path prepended.
folder = []; % Determine where demo folder is (works with all versions).
fullFileName = fullfile(folder, baseFileName);
%===============================================================================
% Read in a demo image.
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(:, :, 3); % Take blue channel.
end
% Display the image.


subplot(1, 2, 1);
imshow(grayImage, []);
axis on;
axis image;
caption = sprintf('Original Gray Scale Image');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
hp = impixelinfo();
% Set up figure properties:
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0, 0, 1, 1]);
% Get rid of tool bar and pulldown menus that are along top of figure.
% set(gcf, 'Toolbar', 'none', 'Menu', 'none');
% Give a name to the title bar.
set(gcf, 'Name', 'Demo by ImageAnalyst', 'NumberTitle', 'Off')
drawnow;
% Do a bottom hat filter to find the small dark spots.
subplot(1, 2, 2);
histogram(grayImage);
grid on;
se = strel('disk', 15);
filteredImage = imbothat(grayImage, se);
% Display the image.
imshow(filteredImage, []);
histogram(filteredImage);
grid on;
% Binarize the image.
% binaryImage = imbinarize(grayImage); 
binaryImage = filteredImage >35;  % Dtermine number from histogram.
% Make sure there are blobs only between 100 and 800 pixels.
binaryImage = bwareafilt(binaryImage, [100, 800]);
% Display the image.
subplot(1, 2, 2);
imshow(binaryImage, []);
axis on;
axis image;
caption = sprintf('Binary Image');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
hp = impixelinfo();
% Label the image
[labeledImage, numBlobs] = bwlabel(binaryImage);
% Make measurements of bounding box
props = regionprops(labeledImage, 'Area');
allAreas = [props.Area]
sortedAread = sort(allAreas)
caption = sprintf('Binary Image with %d Muscle Fibers', numBlobs);
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');

Best Answer

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MATLAB: Compare the levels of gray

MATLAB: Counting the number of muscle fibres in an image

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