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Image Segmentation Tutorial

by

Image Analyst (view profile)

 

27 Aug 2009 (Updated )

Image Processing Tutorial to demonstrate the basic concepts to beginner users.

Editor's Notes:

This file was selected as MATLAB Central Pick of the Week

BlobsDemo.m
%------------------------------------------------------------------------------------------------
% Demo to illustrate simple blob detection, measurement, and filtering.
% Requires the Image Processing Toolbox (IPT) because it demonstates some functions
% supplied by that toolbox, plus it uses the "coins" demo image supplied with that toolbox.
% If you have the IPT (you can check by typing ver on the command line), you should be able to
% run this demo code simply by copying and pasting this code into a new editor window,
% and then clicking the green "run" triangle on the toolbar.
% Running time = 7.5 seconds the first run and 2.5 seconds on subsequent runs.
% A similar Mathworks demo:
% http://www.mathworks.com/products/image/demos.html?file=/products/demos/shipping/images/ipexprops.html
% Code written and posted by ImageAnalyst, July 2009.  Updated April 2015 for MATLAB release R2015a
%------------------------------------------------------------------------------------------------
% function BlobsDemo()
% echo on;
% Startup code.
tic; % Start timer.
clc; % Clear command window.
clearvars; % Get rid of variables from prior run of this m-file.
fprintf('Running BlobsDemo.m...\n'); % Message sent to command window.
workspace; % Make sure the workspace panel with all the variables is showing.
imtool close all;  % Close all imtool figures.
format long g;
format compact;
captionFontSize = 14;

% Check that user has the Image Processing Toolbox installed.
hasIPT = license('test', 'image_toolbox');
if ~hasIPT
	% User does not have the toolbox installed.
	message = sprintf('Sorry, but you do not seem to have the Image Processing Toolbox.\nDo you want to try to continue anyway?');
	reply = questdlg(message, 'Toolbox missing', 'Yes', 'No', 'Yes');
	if strcmpi(reply, 'No')
		% User said No, so exit.
		return;
	end
end

% Read in a standard MATLAB demo image of coins (US nickles and dimes, which are 5 cent and 10 cent coins)
baseFileName = 'coins.png';
folder = fileparts(which(baseFileName)); % Determine where demo folder is (works with all versions).
fullFileName = fullfile(folder, baseFileName);
if ~exist(fullFileName, 'file')
	% It doesn't exist in the current folder.
	% Look on the search path.
	if ~exist(baseFileName, 'file')
		% It doesn't exist on the search path either.
		% Alert user that we can't find the image.
		warningMessage = sprintf('Error: the input image file\n%s\nwas not found.\nClick OK to exit the demo.', fullFileName);
		uiwait(warndlg(warningMessage));
		fprintf(1, 'Finished running BlobsDemo.m.\n');
		return;
	end
	% Found it on the search path.  Construct the file name.
	fullFileName = baseFileName; % Note: don't prepend the folder.
end
% If we get here, we should have found the image file.
originalImage = imread(fullFileName);
% Check to make sure that it is grayscale, just in case the user substituted their own image.
[rows, columns, numberOfColorChannels] = size(originalImage);
if numberOfColorChannels > 1
	promptMessage = sprintf('Your image file has %d color channels.\nThis demo was designed for grayscale images.\nDo you want me to convert it to grayscale for you so you can continue?', numberOfColorChannels);
	button = questdlg(promptMessage, 'Continue', 'Convert and Continue', 'Cancel', 'Convert and Continue');
	if strcmp(button, 'Cancel')
		fprintf(1, 'Finished running BlobsDemo.m.\n');
		return;
	end
	% Do the conversion using standard book formula
	originalImage = rgb2gray(originalImage);
end

% Display the grayscale image.
subplot(3, 3, 1);
imshow(originalImage);
% Maximize the figure window.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
% Force it to display RIGHT NOW (otherwise it might not display until it's all done, unless you've stopped at a breakpoint.)
drawnow;
caption = sprintf('Original "coins" image showing\n6 nickels (the larger coins) and 4 dimes (the smaller coins).');
title(caption, 'FontSize', captionFontSize);
axis image; % Make sure image is not artificially stretched because of screen's aspect ratio.

% Just for fun, let's get its histogram and display it.
[pixelCount, grayLevels] = imhist(originalImage);
subplot(3, 3, 2);
bar(pixelCount);
title('Histogram of original image', 'FontSize', captionFontSize);
xlim([0 grayLevels(end)]); % Scale x axis manually.
grid on;

% Threshold the image to get a binary image (only 0's and 1's) of class "logical."
% Method #1: using im2bw()
%   normalizedThresholdValue = 0.4; % In range 0 to 1.
%   thresholdValue = normalizedThresholdValue * max(max(originalImage)); % Gray Levels.
%   binaryImage = im2bw(originalImage, normalizedThresholdValue);       % One way to threshold to binary
% Method #2: using a logical operation.
thresholdValue = 100;
binaryImage = originalImage > thresholdValue; % Bright objects will be chosen if you use >.
% ========== IMPORTANT OPTION ============================================================
% Use < if you want to find dark objects instead of bright objects.
%   binaryImage = originalImage < thresholdValue; % Dark objects will be chosen if you use <.

% Do a "hole fill" to get rid of any background pixels or "holes" inside the blobs.
binaryImage = imfill(binaryImage, 'holes');

% Show the threshold as a vertical red bar on the histogram.
hold on;
maxYValue = ylim;
line([thresholdValue, thresholdValue], maxYValue, 'Color', 'r');
% Place a text label on the bar chart showing the threshold.
annotationText = sprintf('Thresholded at %d gray levels', thresholdValue);
% For text(), the x and y need to be of the data class "double" so let's cast both to double.
text(double(thresholdValue + 5), double(0.5 * maxYValue(2)), annotationText, 'FontSize', 10, 'Color', [0 .5 0]);
text(double(thresholdValue - 70), double(0.94 * maxYValue(2)), 'Background', 'FontSize', 10, 'Color', [0 0 .5]);
text(double(thresholdValue + 50), double(0.94 * maxYValue(2)), 'Foreground', 'FontSize', 10, 'Color', [0 0 .5]);

% Display the binary image.
subplot(3, 3, 3);
imshow(binaryImage); 
title('Binary Image, obtained by thresholding', 'FontSize', captionFontSize); 

% Identify individual blobs by seeing which pixels are connected to each other.
% Each group of connected pixels will be given a label, a number, to identify it and distinguish it from the other blobs.
% Do connected components labeling with either bwlabel() or bwconncomp().
labeledImage = bwlabel(binaryImage, 8);     % Label each blob so we can make measurements of it
% labeledImage is an integer-valued image where all pixels in the blobs have values of 1, or 2, or 3, or ... etc.
subplot(3, 3, 4);
imshow(labeledImage, []);  % Show the gray scale image.
title('Labeled Image, from bwlabel()', 'FontSize', captionFontSize);

% Let's assign each blob a different color to visually show the user the distinct blobs.
coloredLabels = label2rgb (labeledImage, 'hsv', 'k', 'shuffle'); % pseudo random color labels
% coloredLabels is an RGB image.  We could have applied a colormap instead (but only with R2014b and later)
subplot(3, 3, 5);
imshow(coloredLabels);
axis image; % Make sure image is not artificially stretched because of screen's aspect ratio.
caption = sprintf('Pseudo colored labels, from label2rgb().\nBlobs are numbered from top to bottom, then from left to right.');
title(caption, 'FontSize', captionFontSize);

% Get all the blob properties.  Can only pass in originalImage in version R2008a and later.
blobMeasurements = regionprops(labeledImage, originalImage, 'all');
numberOfBlobs = size(blobMeasurements, 1);

% bwboundaries() returns a cell array, where each cell contains the row/column coordinates for an object in the image.
% Plot the borders of all the coins on the original grayscale image using the coordinates returned by bwboundaries.
subplot(3, 3, 6);
imshow(originalImage);
title('Outlines, from bwboundaries()', 'FontSize', captionFontSize); 
axis image; % Make sure image is not artificially stretched because of screen's aspect ratio.
hold on;
boundaries = bwboundaries(binaryImage);
numberOfBoundaries = size(boundaries, 1);
for k = 1 : numberOfBoundaries
	thisBoundary = boundaries{k};
	plot(thisBoundary(:,2), thisBoundary(:,1), 'g', 'LineWidth', 2);
end
hold off;

textFontSize = 14;	% Used to control size of "blob number" labels put atop the image.
labelShiftX = -7;	% Used to align the labels in the centers of the coins.
blobECD = zeros(1, numberOfBlobs);
% Print header line in the command window.
fprintf(1,'Blob #      Mean Intensity  Area   Perimeter    Centroid       Diameter\n');
% Loop over all blobs printing their measurements to the command window.
for k = 1 : numberOfBlobs           % Loop through all blobs.
	% Find the mean of each blob.  (R2008a has a better way where you can pass the original image
	% directly into regionprops.  The way below works for all versions including earlier versions.)
	thisBlobsPixels = blobMeasurements(k).PixelIdxList;  % Get list of pixels in current blob.
	meanGL = mean(originalImage(thisBlobsPixels)); % Find mean intensity (in original image!)
	meanGL2008a = blobMeasurements(k).MeanIntensity; % Mean again, but only for version >= R2008a
	
	blobArea = blobMeasurements(k).Area;		% Get area.
	blobPerimeter = blobMeasurements(k).Perimeter;		% Get perimeter.
	blobCentroid = blobMeasurements(k).Centroid;		% Get centroid one at a time
	blobECD(k) = sqrt(4 * blobArea / pi);					% Compute ECD - Equivalent Circular Diameter.
	fprintf(1,'#%2d %17.1f %11.1f %8.1f %8.1f %8.1f % 8.1f\n', k, meanGL, blobArea, blobPerimeter, blobCentroid, blobECD(k));
	% Put the "blob number" labels on the "boundaries" grayscale image.
	text(blobCentroid(1) + labelShiftX, blobCentroid(2), num2str(k), 'FontSize', textFontSize, 'FontWeight', 'Bold');
end

% Now, I'll show you another way to get centroids.
% We can get the centroids of ALL the blobs into 2 arrays,
% one for the centroid x values and one for the centroid y values.
allBlobCentroids = [blobMeasurements.Centroid];
centroidsX = allBlobCentroids(1:2:end-1);
centroidsY = allBlobCentroids(2:2:end);
% Put the labels on the rgb labeled image also.
subplot(3, 3, 5);
for k = 1 : numberOfBlobs           % Loop through all blobs.
	text(centroidsX(k) + labelShiftX, centroidsY(k), num2str(k), 'FontSize', textFontSize, 'FontWeight', 'Bold');
end

% Now I'll demonstrate how to select certain blobs based using the ismember() function.
% Let's say that we wanted to find only those blobs
% with an intensity between 150 and 220 and an area less than 2000 pixels.
% This would give us the three brightest dimes (the smaller coin type).
allBlobIntensities = [blobMeasurements.MeanIntensity];
allBlobAreas = [blobMeasurements.Area];
% Get a list of the blobs that meet our criteria and we need to keep.
% These will be logical indices - lists of true or false depending on whether the feature meets the criteria or not.
% for example [1, 0, 0, 1, 1, 0, 1, .....].  Elements 1, 4, 5, 7, ... are true, others are false.
allowableIntensityIndexes = (allBlobIntensities > 150) & (allBlobIntensities < 220);
allowableAreaIndexes = allBlobAreas < 2000; % Take the small objects.
% Now let's get actual indexes, rather than logical indexes, of the  features that meet the criteria.
% for example [1, 4, 5, 7, .....] to continue using the example from above.
keeperIndexes = find(allowableIntensityIndexes & allowableAreaIndexes);
% Extract only those blobs that meet our criteria, and
% eliminate those blobs that don't meet our criteria.
% Note how we use ismember() to do this.  Result will be an image - the same as labeledImage but with only the blobs listed in keeperIndexes in it.
keeperBlobsImage = ismember(labeledImage, keeperIndexes);
% Re-label with only the keeper blobs kept.
labeledDimeImage = bwlabel(keeperBlobsImage, 8);     % Label each blob so we can make measurements of it
% Now we're done.  We have a labeled image of blobs that meet our specified criteria.
subplot(3, 3, 7);
imshow(labeledDimeImage, []);
axis image;
title('"Keeper" blobs (3 brightest dimes in a re-labeled image)', 'FontSize', captionFontSize);

% Plot the centroids in the original image in the upper left.
% Dimes will have a red cross, nickels will have a blue X.
message = sprintf('Now I will plot the centroids over the original image in the upper left.\nPlease look at the upper left image.');
reply = questdlg(message, 'Plot Centroids?', 'OK', 'Cancel', 'Cancel');
% Note: reply will = '' for Upper right X, 'OK' for OK, and 'Cancel' for Cancel.
if strcmpi(reply, 'Cancel')
	return;
end
subplot(3, 3, 1);
hold on; % Don't blow away image.
for k = 1 : numberOfBlobs           % Loop through all keeper blobs.
	% Identify if blob #k is a dime or nickel.
	itsADime = allBlobAreas(k) < 2200; % Dimes are small.
	if itsADime
		% Plot dimes with a red +.
		plot(centroidsX(k), centroidsY(k), 'r+', 'MarkerSize', 10, 'LineWidth', 2);
	else
		% Plot dimes with a blue x.
		plot(centroidsX(k), centroidsY(k), 'bx', 'MarkerSize', 10, 'LineWidth', 2);
	end
end


% Now use the keeper blobs as a mask on the original image.
% This will let us display the original image in the regions of the keeper blobs.
maskedImageDime = originalImage; % Simply a copy at first.
maskedImageDime(~keeperBlobsImage) = 0;  % Set all non-keeper pixels to zero.
subplot(3, 3, 8);
imshow(maskedImageDime);
axis image;
title('Only the 3 brightest dimes from the original image', 'FontSize', captionFontSize);

% Now let's get the nickels (the larger coin type).
keeperIndexes = find(allBlobAreas > 2000);  % Take the larger objects.
% Note how we use ismember to select the blobs that meet our criteria.
nickelBinaryImage = ismember(labeledImage, keeperIndexes);
% Let's get the nickels from the original grayscale image, with the other non-nickel pixels blackened.
% In other words, we will create a "masked" image.
maskedImageNickel = originalImage; % Simply a copy at first.
maskedImageNickel(~nickelBinaryImage) = 0;  % Set all non-nickel pixels to zero.
subplot(3, 3, 9);
imshow(maskedImageNickel, []);
axis image;
title('Only the nickels from the original image', 'FontSize', captionFontSize);

elapsedTime = toc;
% Alert user that the demo is done and give them the option to save an image.
message = sprintf('Done making measurements of the features.\n\nElapsed time = %.2f seconds.', elapsedTime);
message = sprintf('%s\n\nCheck out the figure window for the images.\nCheck out the command window for the numerical results.', message);
message = sprintf('%s\n\nDo you want to save the pseudo-colored image?', message);
reply = questdlg(message, 'Save image?', 'Yes', 'No', 'No');
% Note: reply will = '' for Upper right X, 'Yes' for Yes, and 'No' for No.
if strcmpi(reply, 'Yes')
	% Ask user for a filename.
	FilterSpec = {'*.PNG', 'PNG Images (*.png)'; '*.tif', 'TIFF images (*.tif)'; '*.*', 'All Files (*.*)'};
	DialogTitle = 'Save image file name';
	% Get the default filename.  Make sure it's in the folder where this m-file lives.
	% (If they run this file but the cd is another folder then pwd will show that folder, not this one.
	thisFile = mfilename('fullpath');
	[thisFolder, baseFileName, ext] = fileparts(thisFile);
	DefaultName = sprintf('%s/%s.tif', thisFolder, baseFileName);
	[fileName, specifiedFolder] = uiputfile(FilterSpec, DialogTitle, DefaultName);
	if fileName ~= 0
		% Parse what they actually specified.
		[folder, baseFileName, ext] = fileparts(fileName);
		% Create the full filename, making sure it has a tif filename.
		fullImageFileName = fullfile(specifiedFolder, [baseFileName '.tif']);
		% Save the labeled image as a tif image.
		imwrite(uint8(coloredLabels), fullImageFileName);
		% Just for fun, read image back into the imtool utility to demonstrate that tool.
		tifimage = imread(fullImageFileName);
		imtool(tifimage, []);
	end
end

message = sprintf('Would you like to crop out each coin to individual images?');
reply = questdlg(message, 'Extract Individual Images?', 'Yes', 'No', 'Yes');
% Note: reply will = '' for Upper right X, 'Yes' for Yes, and 'No' for No.
if strcmpi(reply, 'Yes')
	figure;	% Create a new figure window.
	% Maximize the figure window.
	set(gcf, 'Units','Normalized','OuterPosition',[0 0 1 1]);
	for k = 1 : numberOfBlobs           % Loop through all blobs.
		% Find the bounding box of each blob.
		thisBlobsBoundingBox = blobMeasurements(k).BoundingBox;  % Get list of pixels in current blob.
		% Extract out this coin into it's own image.
		subImage = imcrop(originalImage, thisBlobsBoundingBox);
		% Determine if it's a dime (small) or a nickel (large coin).
		if blobMeasurements(k).Area > 2200
			coinType = 'nickel';
		else
			coinType = 'dime';
		end
		% Display the image with informative caption.
		subplot(3, 4, k);
		imshow(subImage);
		caption = sprintf('Coin #%d is a %s.\nDiameter = %.1f pixels\nArea = %d pixels', ...
			k, coinType, blobECD(k), blobMeasurements(k).Area);
		title(caption, 'FontSize', textFontSize);
	end

	% Display the MATLAB "peaks" logo.
	logoFig = subplot(3, 4, 11:12);
	caption = sprintf('A MATLAB Tutorial by ImageAnalyst');
	text(0.5,1.15, caption, 'Color','r', 'FontSize', 18, 'FontWeight','b', 'HorizontalAlignment', 'Center') ;
	positionOfLowerRightPlot = get(logoFig, 'position');
	L = 40*membrane(1,25);
	logoax = axes('CameraPosition', [-193.4013 -265.1546  220.4819],...
		'CameraTarget',[26 26 10], ...
		'CameraUpVector',[0 0 1], ...
		'CameraViewAngle',9.5, ...
		'DataAspectRatio', [1 1 .9],...
		'Position', positionOfLowerRightPlot, ...
		'Visible','off', ...
		'XLim',[1 51], ...
		'YLim',[1 51], ...
		'ZLim',[-13 40], ...
		'parent',gcf);
	s = surface(L, ...
		'EdgeColor','none', ...
		'FaceColor',[0.9 0.2 0.2], ...
		'FaceLighting','phong', ...
		'AmbientStrength',0.3, ...
		'DiffuseStrength',0.6, ... 
		'Clipping','off',...
		'BackFaceLighting','lit', ...
		'SpecularStrength',1, ...
		'SpecularColorReflectance',1, ...
		'SpecularExponent',7, ...
		'Tag','TheMathWorksLogo', ...
		'parent',logoax);
	l1 = light('Position',[40 100 20], ...
		'Style','local', ...
		'Color',[0 0.8 0.8], ...
		'parent',logoax);
	l2 = light('Position',[.5 -1 .4], ...
		'Color',[0.8 0.8 0], ...
		'parent',logoax);
end

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