How could I extract a defect from a semiconductor image?

I have this image of a semiconductor wafer and it has a defect in it which I need to detect using Matlab. I should detect only it, and not its background. I need to also measure its perimeter and area.

So far I have this code where I convert the original image to a binary image and then use dilation on it and then try to get its contour. When getting the perimeter and the area, I receive the outcome not only for the defect, but also for the rest of the image, which is not what I want. How can I extract the defect only so I can get only its area and perimeter?

The image:

fig3 = imread('figure3.png');
imshow(fig3);

title('Original image', 'FontSize', 18);
% Gray image
fig3Gray = rgb2gray(fig3);

% Binary image
BW3 = imbinarize(fig3Gray,0.5);
imshow(BW3);
title('Binary image', 'FontSize', 18);

se3 = strel('square',5);

% Dilation image
dilated3 = imdilate(BW3,sr);
imshow(dilated3);
title('Dilated image', 'FontSize', 18);

minus3 = ~(BW3-dilated3);
imshow(minus3);
title('Contour image', 'FontSize', 18);
imshowpair(minus3,BW3,'montage');

% Perimeter and Area calculation
Coon3 = bwconncomp(~BW3)
ANS3 = length(Coon3.PixelIdxList{1});
OUTPUT3 = regionprops(Coon3,'Perimeter','Area');
P3 = OUTPUT3.Perimeter
Area3 = OUTPUT3.Area

[KALYAN ACHARJYA]

Is that dark, near-circular blob considered as the defect?

[Angel Hadzhiev]

Yes, this blob is the defect. Everything else is not needed.

[Image Analyst]

What do you want to assume?

1. That the defect is known to be in the upper half of the image?
2. That the defect has a certain aspect ratio?
3. That you have another, "perfect" image that you can compare this one to?

You have to make some assumptions as to what defines a defect.

[Angel Hadzhiev]

I want to assume that the image is in the uppr central half of the image and it has perimter and area. I want to find them and I want to get rid of the background so only the defect stays. Could you help me with that?

Answer 1

[KALYAN ACHARJYA]

May be edge detection, then morpho operation easier in this case

result=~edge(binary_image,'sobel');

Please see bwareaopen and bwareafilt functions, which may helps you.

Hope you can easily segmnet the well distinct region.

[Angel Hadzhiev]

Sadly, this does not help.

Answer 2

[Image Analyst]

If, as you said, being dark and in the upper half of the image are your ONLY criteria, then the code below will find those blobs and measure their areas and perimeters.

% Initialization steps.
clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
imtool close all;  % Close all imtool figures if you have the Image Processing Toolbox.
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;
% 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 demo image.
folder = pwd
baseFileName = 'figure3.png';
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
if ~exist(fullFileName, 'file')
	% Didn't find it there.  Check the search path for it.
	fullFileName = baseFileName; % No path this time.
	if ~exist(fullFileName, 'file')
		% Still didn't find it.  Alert user.
		errorMessage = sprintf('Error: %s does not exist.', fullFileName);
		uiwait(warndlg(errorMessage));
		return;
	end
end
grayImage = imread(fullFileName);
% Get the dimensions of the image.  numberOfColorBands should be = 3.
[rows, columns, numberOfColorBands] = size(grayImage);
if numberOfColorBands > 1
	fprintf('This image is RGB.  I will change it to gray scale.\n');
	grayImage = grayImage(:, :, 2);
end

% Display the original image.
subplot(2, 2, 1);
imshow(grayImage);
axis('on', 'image');
caption = sprintf('Original Image : "%s"', baseFileName);
title(caption, 'FontSize', fontSize);
impixelinfo;
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'Outerposition', [0, 0.1, 1, 0.9], ...
	'Name', 'Demo by Image Analyst', 'NumberTitle', 'Off');

% Display the original image's histogram.
subplot(2, 2, 2);
imhist(grayImage);
grid on;
title('Histogram of Gray Scale Image', 'FontSize', fontSize);

% Get dark stuff only.
binaryImage = grayImage < 50;
% Display the binary image.
subplot(2, 2, 3);
imshow(binaryImage);
axis('on', 'image');
caption = sprintf('Binary Image');
title(caption, 'FontSize', fontSize);

% Make area measurements.
[labeledImage, numBlobs] = bwlabel(binaryImage);
props = regionprops(labeledImage, 'Area', 'Centroid', 'Perimeter');
allreas = [props.Area]
centroids = vertcat(props.Centroid);
xCentroids = centroids(:, 1);
yCentroids = centroids(:, 2);
% Find out which blobs have a centroid in the upper half of the image.
inUpperHalf = find(yCentroids < rows / 2);
% Extract only those blobs.
binaryImage = ismember(labeledImage, inUpperHalf);

% Now re-measure.
[labeledImage, numBlobs] = bwlabel(binaryImage);
props = regionprops(labeledImage, 'Area', 'Centroid', 'Perimeter');
allreas = [props.Area]
allPerimeters = [props.Perimeter]
centroids = vertcat(props.Centroid);
xCentroids = centroids(:, 1);
yCentroids = centroids(:, 2);

% Apply a variety of pseudo-colors to the regions.
coloredLabelsImage = label2rgb (labeledImage, 'hsv', 'k', 'shuffle');
% Display the pseudo-colored image.
subplot(2, 2, 4);
imshow(coloredLabelsImage);
axis('on', 'image');
caption = sprintf('Upper Half Defects');
title(caption, 'FontSize', fontSize);