A binary image having boundaries, I want to separate each regions from the image and find out the area of each segment.

Splitting an image based on boundaries having in an image. And then applying different region property of each splitted regions. An input image is given here.

Answer 1

[Image Analyst]

See my Image Processing Tutorial in my File Exchange: https://www.mathworks.com/matlabcentral/fileexchange/?term=authorid%3A31862&sort=downloads_desc

Then run this code:

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 = 20;

%===============================================================================
% Read in gray scale demo image.
folder = pwd; % Determine where demo folder is (works with all versions).
baseFileName = 'abc.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
rgbImage = 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(rgbImage)
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(rgbImage);
  % 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 = rgbImage(:, :, 2); % Take green channel.
else
  grayImage = rgbImage; % It's already gray scale.
end
% Now it's gray scale with range of 0 to 255.

% Display the image.
subplot(2, 2, 1);
imshow(grayImage, []);
title('Original Image', 'FontSize', fontSize, 'Interpreter', 'None');
axis('on', 'image');
hp = impixelinfo();

%------------------------------------------------------------------------------
% Set up figure properties:
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0, 0.04, 1, 0.96]);
% 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;

% Binarize the image
binaryImage = ~imbinarize(uint8(grayImage));

% Display the image.
subplot(2, 2, 2);
imshow(binaryImage, []);
title('Binary Image', 'FontSize', fontSize, 'Interpreter', 'None');
axis('on', 'image');
hp = impixelinfo();
drawnow;

% Label the image
labeledImage = bwlabel(binaryImage);
% Label each blob with 8-connectivity, so we can make measurements of it
[labeledImage, numberOfBlobs] = bwlabel(binaryImage, 8);
% Apply a variety of pseudo-colors to the regions.
coloredLabelsImage = label2rgb (labeledImage, 'hsv', 'k', 'shuffle');

% Display the pseudo-colored image.
subplot(2, 2, 3);
imshow(coloredLabelsImage, []);
title('Labeled Image', 'FontSize', fontSize, 'Interpreter', 'None');
axis('on', 'image');
hp = impixelinfo();

% Get all the blob properties.  Can only pass in originalImage in version R2008a and later.
props = regionprops(labeledImage, 'all');
numberOfBlobs = size(props, 1);
% Get all the areas
allAreas = [props.Area]
% Get all the Perimeters
allPerimeters = [props.Perimeter]
% Get all the Solidities
allSolidities = [props.Solidity]

% Show histogram of areas:
subplot(2, 2, 4);
histogram(allAreas);
grid on;
title('Area Distribution', 'FontSize', fontSize, 'Interpreter', 'None');
xlabel('Area', 'FontSize', fontSize);
ylabel('Count', 'FontSize', fontSize);

[NASU NASU]

First of all thank you sir. Now my question is if I am interested only for yellow (center portion) and want to extract only that portion then what should I do?

[Image Analyst]

It looks like it would be at (x,y) = [columns/2, rows/2]. If it moves around from image to image, you'd have to specify what it is about that shape that makes it different from all the other shapes, like it's the one closest to the center of the image or its area is closest to some known area, or something about the shape of it. What is unique about the center/yellow blob that we can use to identify it?

[NASU NASU]

Sir, I only want to extract yellow portion and save it as binary image. I will do further process on only that portion (Region of interest).

Thank You

[Image Analyst]

The yellow color is arbitrary - it could be any color and any color could be anywhere in a different image. I'm going to assume that you want the blob with its centroid closest to the center of the image. To do that, see attached code.

[NASU NASU]

Waoo, Its great sir, thanks a lot, Thank you for your kind help.

[NASU NASU]

[Image Analyst]

That's because this image has a blob that completely encircles the image. And it's center, even though not in the blob, is slightly closer to the center of the image than the one in the center of the image. You can get rid of the outer blob by calling imclearborder(). See attached code.

Answer 2

[NASU NASU]

[NASU NASU]

But Sir, here I am not getting center blob, extracted image. Please help me.