science project on counting shapes

I've been using the attached image and blobs demo to look at the attached image. I need to count the number of light colored circles/shapes in the image. THe image I uploaded is natively a TIF, but I converted to it to JPG so I could upload it. I'm over my head on this one. After two weeks, I'm no where on this one. I know the steps based on other posts is below but that has not helped since I cant get any individual steps to complete with any meaningful output.

• # # # Binarize the image so that it's logical. binaryImage = grayImage > 128
• # # # Call binaryImage = imclearborder(binaryImage) to get rid of the single line around the perimeter.
• # # # Invert the image: binaryImage = ~binaryImage, so now black circles are white
• # # # Call binaryImage = imclearborder(binaryImage) to get rid of the large (now white) background
• # # # Call bwlabel: [labeledImage, numberOfCircles] = bwlabel(binaryImage). This gives you the count.
• # # #

Answer 1

[Image Analyst]

Maybe try a top hat filter. Or else try adapthisteq() to try to flatten out the background.

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 = 20;

filename = '10mgml.jpg';
fullFileName = fullfile(filename);
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, []);
axis on;
title('Original Grayscale Image', 'FontSize', fontSize, 'Interpreter', 'None');

% Crop off the annotation at the bottom.
grayImage = grayImage(1:213, :);

% 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') 

% Let's compute and display the histogram.
[pixelCount, grayLevels] = imhist(grayImage);
subplot(2, 2, 2); 
bar(grayLevels, pixelCount); % Plot it as a bar chart.
grid on;
title('Histogram of original image', 'FontSize', fontSize, 'Interpreter', 'None');
xlabel('Gray Level', 'FontSize', fontSize);
ylabel('Pixel Count', 'FontSize', fontSize);
xlim([0 grayLevels(end)]); % Scale x axis manually.

% Do a tophat filter
se = strel('disk', 2, 0);
filteredImage = imtophat(grayImage, se);
% Display the image.
subplot(2, 2, 3);
imshow(filteredImage, []);
axis on;
title('Top Hat Filtered Image', 'FontSize', fontSize, 'Interpreter', 'None');

% Let's compute and display the histogram.
[pixelCount, grayLevels] = imhist(filteredImage);
subplot(2, 2, 2); 
bar(grayLevels, pixelCount); % Plot it as a bar chart.
grid on;
title('Histogram of top hat filtered image', 'FontSize', fontSize, 'Interpreter', 'None');
xlabel('Gray Level', 'FontSize', fontSize);
ylabel('Pixel Count', 'FontSize', fontSize);
xlim([0 grayLevels(end)]); % Scale x axis manually.

% Binarize the image
binaryImage = filteredImage > 10;
% Display the image.
subplot(2, 2, 4);
imshow(binaryImage, []);
axis on;
title('Binary Image', 'FontSize', fontSize, 'Interpreter', 'None');

[Benjamin Baynard]

That does pull out the image correctly I believe. How can I add another area on the output for a count of the shapes? That was something on the blob demo that I could not figure out at all.