My project is segmenting circle part of blood cell image and calculating the Area, Perimeter, eccentricity of the circle object ..

please help me to how find the circle object in below image and also calculate the Area, Perimeter, eccentricity of the circle object..... I used MATLAB version 7.8.0 (2009a) image 1:

image 2:

[Nivetha m]

its very important work for me plz answer the question

Answer 1

[Image Analyst]

Did you try imfindcircles()?

Otherwise, threshold, fill blobs, get area and perimeter, compute circularity, and then use ismember() to extract only those blobs that are round. See my Image Segmentation Tutorial. http://www.mathworks.com/matlabcentral/fileexchange/?term=authorid%3A31862

[Nivetha m]

imfincircle function not working i use matlab version 7.8.0 (2009a)

[Nivetha m]

i try your demo its working but its not identify circle cell in my image , my image circle is small...

[Image Analyst]

If you want only the circular objects, then get the area and perimeter and compute the circularity

[labeledImage, numObjects] = bwlabel(binaryImage);
props = regionprops(labeledImage, 'Area', 'Perimeter');
allAreas = [props.Area];
allPerims = [props.Perimeter];
circularities = 4 * pi * allPerims .^ 2 ./ allAreas;
indexesOfRoundBlobs = find(circularities > 3)
newBinaryImage = ismember(labeledImage, indexesOfRoundBlobs ) > 0;
% Now have binary image of round blobs ONLY.
% So now relabel and remeasure just those.
[labeledImage, numRoundObjects] = bwlabel(newBinaryImage);
props = regionprops(labeledImage, 'Area', 'Perimeter');

Adjust the parameter 3 to be whatever you want depending on how round you want the things to be.

[Nivetha m]

hi i did some modified your code i get below output image but i want to be separate the connecting circles please help me to solve my problem

code:

%------------------------------------------------------------------------------------------------
% 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)
imagen=imread('C:\Users\shankar\Documents\MATLAB\filtering\c1.JPG'); %%imagen   365x550   200750  logical   
%%Show image
figure(1),imshow(imagen),title('INPUT IMAGE WITH NOISE')
%%Convert to gray scale
if size(imagen,3)==3 % RGB image
  imagen=rgb2gray(imagen);
end
%%Convert to binary image
threshold = graythresh(imagen);
originalImage =~im2bw(imagen,threshold);
%%Remove all object containing fewer than 30 pixels
binaryImage = bwareaopen(originalImage,30);
pause(1)
%%Show image binary image
subplot(3, 3, 3),imshow(~binaryImage),title('INPUT IMAGE WITHOUT NOISE')
% 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 cell 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('cell #%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

[Nivetha m]

please give solution its very important work for my project

[Image Analyst]

To separate touching blobs, use watershed():

http://blogs.mathworks.com/steve/2013/11/19/watershed-transform-question-from-tech-support/

http://www.mathworks.com/help/images/examples/marker-controlled-watershed-segmentation.html

http://cmm.ensmp.fr/~beucher/wtshed.html

[haniz azwa]

I try your coding example, actually, I'm currently working on my final year project. your coding is working but it cannot identify the abnormal cell. May I know which part did you adjust your own parameter i mean the exact value of your cell?