MATLAB: How to Separate the RGB channels from an image and get the average of each RGB channel separetly

remove backgroundrgb channelrgb channel average

How to Separate the RGB channels from an image and get the average of each RGB channel separately

Hello MATLAB community,

I have a set of 20 images, which I need to process simultaneously. All the images have a stain of red colourant in the middle, which I need to separate from the white background. I need to obtain the RGB value average of each channel (pixels) of the stained red area. I am new to the MATLAB world. Hence I am a bit unsure how to proceed. I leave an example of one of the images I have.

Thank you for your time. I appreciate all your help.

Best Answer

I'm feeling particularly lazy today, so I'm not going out of my way to make this solution avoid nonstandard tools. They could be replaced, but let's see what I came up with.

% read images
inpict=imread('stain.jpg');
% use thresholding in chroma to isolate red areas
mask=mono(inpict,'clch')>0.25*255;
% get rid of fabric patterning from bleed
mask=imopen(mask,strel('disk',10));
% pick only the central blob
mask=bwareafilt(mask,1);
% fill in any holes before erosion
mask=imclose(mask,strel('disk',10));
% erode to get rid of any remaining light edges
mask=imerode(mask,strel('disk',10));
% display masked region
maskedpict=inpict.*uint8(mask);
imshow(maskedpict)
% calculate mean color tuple
selectedpix=inpict(repmat(mask,[1 1 3]));
meancolor=mean(reshape(selectedpix,[numel(selectedpix)/3 3]))/255

This will isolate the central part of the stain

and the mean color tuple (normalized) is dumped to console:

meancolor =
    0.3814    0.0423    0.0695

So at least for this image, it works. The thresholding and strel sizes may need to be adjusted. If you only ever need one size of strel, you can just save it and reuse it instead of regenerating it every time. I just did that in case they needed tweaking.

mono() is part of the MIMT, which can be downloaded here if desired.

https://www.mathworks.com/matlabcentral/fileexchange/53786-image-manipulation-toolbox?s_tid=srchtitle

In this example, I'm thresholding chroma in LCHab. You could do the same thing using rgb2lab() and then converting to polar coordinates, etc. The main point is to leverage the lightness/color separation of the color model to avoid needing to deal with the shadows.

EDIT: I got to thinking that depending on your goals, it may be beneficial to further process the ROI. Removing specular highlights from the wet gauze would help keep them from lightening/desaturating your result. Furthermore, the materials used in the gauze would tend to skew the results a bit. It may be more meaningful to use a median of the sample area.

Consider this example.

% remove specular highlights from the mask
mpc=mono(maskedpict,'y')<0.2*255;
maskedpict2=inpict.*uint8(mpc & mask);
% get mean, median, and mode colors (imstats is from MIMT)
selectedpix=inpict(repmat(mpc & mask,[1 1 3]));
[mn md mo]=imstats(im2double(reshape(selectedpix,[numel(selectedpix)/3 1 3])), ...
	'mean','median','modecolor')

Using the mean, median, and mode colors returned for both cases, I threw together this sample comparison image:

Against the white background of this website, it's a bit hard to tell, but sampling the mean is most susceptible to the influence of the specular highlights. The mode color is least affected.

Related Solutions

MATLAB: Obtain average image threshold without the background

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;
%--------------------------------------------------------------------------------------------------------

%    READ IN IMAGE
folder = pwd;
baseFileName = 'image.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);
		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
% Display the image.
subplot(2, 2, 1);
imshow(grayImage, []);
title('Original Grayscale Image', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
hFig = gcf;
hFig.WindowState = 'maximized'; % May not work in earlier versions of MATLAB.
drawnow;
%--------------------------------------------------------------------------------------------------------
%    SEGMENTATION OF IMAGE
% Get a binary image of just the blob and not including the black background or the white surround.
mask = grayImage > 0 & grayImage < 255;  %imbinarize(grayImage);
subplot(2, 2, 2);
imshow(mask, []);
impixelinfo;
title('Initial Mask Image', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
% Display histogram of image but only the image within the mask
subplot(2, 3, 4);
histogram(grayImage(mask), 0:255);
grid on;
title('Histogram of gray image', 'FontSize', fontSize);
% Now get the threshold
thresh = 255 * graythresh(grayImage(mask))
% Put a red line there at the threshold
xline(thresh, 'Color', 'r', 'LineWidth', 2);
% Get a binary image of the bright stuff
brightStuff = grayImage >= thresh & mask;
subplot(2, 3, 5);
imshow(brightStuff, []);
impixelinfo;
title('Bright Pixels in Region', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
subplot(2, 3, 6);
darkStuff = ~brightStuff & mask;
imshow(darkStuff, []);
impixelinfo;
title('Dark Pixels in Region', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;

MATLAB: Segment Image based on Color pixels.

Try this:

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 16;
%===============================================================================
% Get the name of the image the user wants to use.
baseFileName = 'A1.jpg';
folder = pwd;
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
%=======================================================================================
% Read in demo image.
rgbImage = imread(fullFileName);
% Get the dimensions of the image.
[rows, columns, numberOfColorChannels] = size(rgbImage)
% Display image.
subplot(2, 2, 1);
imshow(rgbImage, []);
impixelinfo;
axis on;
caption = sprintf('Original Color Image\n%s', baseFileName);
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
hp = impixelinfo(); % Set up status line to see values when you mouse over the image.
% Set up figure properties:
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0 0.05 1 0.95]);
% 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;
% Extract the individual red, green, and blue color channels.
redChannel = rgbImage(:, :, 1);
greenChannel = rgbImage(:, :, 2);
blueChannel = rgbImage(:, :, 3);
% get a mask for the red line.
mask = redChannel > greenChannel;
% Display the image.


subplot(2, 2, 2);
imshow(mask);
caption = sprintf('Color Segmentation Mask Image');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
axis('on', 'image');
drawnow;
% fill holes.
mask = imfill(mask, 'holes');
% Take the largest blob only.
mask = bwareafilt(mask, 1);
% Shrink two layers of pixels to come inside the red lines.
mask = imerode(mask, true(5));
% Display the image.
subplot(2, 2, 3);
imshow(mask);
title('Final Mask', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
axis('on', 'image');
% Get a masked image.
% Mask the image using bsxfun() function to multiply the mask by each channel individually.
maskedRgbImage = bsxfun(@times, rgbImage, cast(mask, 'like', rgbImage));
% Display the image.
subplot(2, 2, 4);
imshow(maskedRgbImage);
title('Masked RGB Image', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
axis('on', 'image');
% Tell user the answer.
message = sprintf('Done!');
uiwait(helpdlg(message));

Best Answer

Related Solutions

MATLAB: Obtain average image threshold without the background

MATLAB: Segment Image based on Color pixels.

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