MATLAB: How can i detect every blob

blobscirclefeature extractImage Processing Toolbox

Hello guys and Image analyst. Why cannot my code detect every circle as blob ? As it only detect 1 blob.

Here my code, if you can modify it thanks !

im = imread('AML1.bmp');
cform = makecform('srgb2lab');
lab_he = applycform(im,cform);
ab = double(lab_he(:,:,2:3));
nrows = size(ab,1);
ncols = size(ab,2);
ab = reshape(ab,nrows*ncols,2);
nColors = 3;
% repeat the clustering 3 times to avoid local minima
[cluster_idx, cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean', ...
                                      'Replicates',3);
pixel_labels = reshape(cluster_idx,nrows,ncols);
figure
imshow(pixel_labels,[]), title('image labeled by cluster index');
segmented_images = cell(1,3);
rgb_label = repmat(pixel_labels,[1 1 3]);
for k = 1:nColors
    color = im;
    color(rgb_label ~= k) = 255; % 255=putih, 0=hitam
    segmented_images{k} = color;
end
figure
imshow(segmented_images{1}), title('objects in cluster 1');
figure
imshow(segmented_images{2}), title('objects in cluster 2');
figure
imshow(segmented_images{3}), title('objects in cluster 3');
mean_cluster_value = mean(cluster_center,2);
[tmp, idx] = sort(mean_cluster_value);
blue_cluster_num = idx(1);
L = lab_he(:,:,1);
blue_idx = find(pixel_labels == blue_cluster_num);
L_blue = L(blue_idx);
is_light_blue = imbinarize(L_blue);
nuclei_labels = repmat(uint8(0),[nrows ncols]);
nuclei_labels(blue_idx(is_light_blue==false)) = 1;
nuclei_labels = repmat(nuclei_labels,[1 1 3]);
blue_nuclei = im;
blue_nuclei(nuclei_labels ~= 1) = 255;
figure
imshow(blue_nuclei), title('blue nuclei');
I1 = rgb2gray(im);
I2 = rgb2gray(blue_nuclei);
points1 = detectHarrisFeatures(I1);
points2 = detectHarrisFeatures(I2);
[features1,valid_points1] = extractFeatures(I1,points1);
[features2,valid_points2] = extractFeatures(I2,points2);
indexPairs = matchFeatures(features1,features2);
matchedPoints1 = valid_points1(indexPairs(:,1),:);
matchedPoints2 = valid_points2(indexPairs(:,2),:);
figure; showMatchedFeatures(I1,I2,matchedPoints1,matchedPoints2);
a =2; %rows
b =2; %columns
I = blue_nuclei;
I2 = rgb2gray(I);
originalImage = mat2gray(I2);
imshow(originalImage);
I_eq = adapthisteq(originalImage);
imshow(I_eq);
bw = im2bw(I_eq, graythresh(I_eq));
imshow(bw);
BW = imopen(bw, strel('square', 25));
imshow(BW);
%%From Blob
blobMeasurements = regionprops(BW, BW, '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.
figure, imshow(BW);
title('Outlines, from bwboundaries()'); axis square;
hold on;
boundaries = bwboundaries(BW);  
numberOfBoundaries = size(boundaries);
for k = 1 : numberOfBoundaries
  thisBoundary = boundaries{k};
  plot(thisBoundary(:,2), thisBoundary(:,1), 'g', 'LineWidth', 2);
end
hold off;
fontSize = 12;  % 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));
end

Best Answer

Make sure your blobs are the standard white, and not black. Try

BW = ~BW;

Related Solutions

MATLAB: Separate two type of object in the image

Do you mean like 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 = 'Q4_4.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, 3, 1);
imshow(grayImage);
axis('on', 'image');
title('Original Grayscale Image', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
hFig = gcf;
hFig.WindowState = 'maximized'; % May not work in earlier versions of MATLAB.
drawnow;
%--------------------------------------------------------------------------------------------------------
% Binarize the image.
binaryImage = ~imbinarize(grayImage);
% Get rid of particles less than 200 in area.
binaryImage = bwareaopen(binaryImage, 200);
% Eliminate partial blobs - those touching the edge of the image.
binaryImage = imclearborder(binaryImage);
subplot(2, 3, 2);
imshow(binaryImage, []);
axis('on', 'image');
title('Mask Image', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
drawnow;
% Find blobs
props = regionprops(binaryImage, 'Centroid', 'Area');
xy = vertcat(props.Centroid)
allAreas = [props.Area];
subplot(2, 3, 3);
histogram(allAreas, 10);
grid on;
title('Histogram of Blob Areas', 'FontSize', fontSize, 'Interpreter', 'None');
xlabel('Area', 'FontSize', fontSize, 'Interpreter', 'None');
ylabel('Count', 'FontSize', fontSize, 'Interpreter', 'None');
% Get blobs smaller than 2500
[labeledImage, numBlobs] = bwlabel(binaryImage);
smallIndexes = find(allAreas < 2500);
largeIndexes = find(allAreas >= 2500);
% Extract the small blobs with ismember()
smallBlobs = ismember(labeledImage, smallIndexes);
subplot(2, 3, 4);
imshow(smallBlobs);
title('Small Blobs', 'FontSize', fontSize, 'Interpreter', 'None');
% Extract the large blobs with ismember()
largeBlobs = ismember(labeledImage, largeIndexes);
subplot(2, 3, 5);
imshow(largeBlobs);
title('Large Blobs', 'FontSize', fontSize, 'Interpreter', 'None');

MATLAB: Center of Mass, metal foam

The key to getting the centroid of all the blobs together instead of getting each individually is to make a labeled image where the image is floating point, not binary. If it's binary, regionprops() will internally label each connected component and report the centroids of each component individually, which you said you don't want when you said "center of mass of the black circles" (at least that's how I'm interpreting it).

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;
%--------------------------------------------------------------------------------------------------------
%    EXTRACTION OF IMAGE
% Get a binary image
binaryImage = grayImage < 128;
% Get rid of the surround.
binaryImage = imclearborder(binaryImage);
subplot(2, 2, 2);
imshow(grayImage, []);
impixelinfo;
title('Binary Image', 'FontSize', fontSize, 'Interpreter', 'None');
impixelinfo;
% Fill holes
binaryImage = imfill(binaryImage, 'holes');
% Display the image.
subplot(2, 2, 3);
imshow(binaryImage, []);
title('Black Blobs', 'FontSize', fontSize, 'Interpreter', 'None');
axis('on', 'image');
% Find the center of mass, which is the same as the centroid since all we have is a binary image.
% Casting the binary image to double is the KEY to making sure we get just one
% centroid rather than a centroid for each and every blob individually.
labeledImage = double(binaryImage);
props = regionprops(labeledImage, 'Centroid')
xCentroid = props.Centroid(1);
yCentroid = props.Centroid(2);
% Plot a red crosshairs there
hold on;
plot(xCentroid, yCentroid, 'r+', 'MarkerSize', 200, 'LineWidth', 3);
caption = sprintf('Centroid of black blobs at (%.2f, %.2f)', xCentroid, yCentroid);
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');

If you actually want all the individual centroids, you can do

labeledImage = double(binaryImage);

but you said you already did that and have the individual centroids already.

Best Answer

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MATLAB: Separate two type of object in the image

MATLAB: Center of Mass, metal foam

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