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Find circularity in low signal to noise image with light relfections

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I would like to find the circularity of the patch in the image.
I know that light reflections should be avoided using polarizers during the image taking process, but I cannot retake these images so I have to analyze what I have.
I have tried the following, which kind of works, however when there are image reflections near the edge of the patch, the circularity (0 to 1) is under captured because it accounts for out of the patch shapes.
Any help or suggestion to improve the code would be appreciated.
Thank you,
title('Original image')
% Smooth the bright regions by creating a bright mask
brightMask = IG > thr;
windowWidth = 30;
kernel = ones(windowWidth, windowWidth) / windowWidth^2;
blurryIG = imfilter(IG, kernel);
% Binarize and fill gaps
BW2 = bwareaopen(BW,10000);
BW3 = bwareaopen(BWerode,10000);
%% Plot

Akzeptierte Antwort

Image Analyst
Image Analyst am 30 Mai 2023
Try this. Adjust parameters as needed to get what you want.
% Demo by Image Analyst
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
clear all;
workspace; % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 16;
% Get the name of the image the user wants to use.
baseFileName = 'B1.png';
folder = pwd;
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);
% Read in image.
rgbImage = imread(fullFileName);
% Get the dimensions of the image.
[rows, columns, numberOfColorChannels] = size(rgbImage)
% Display image.
subplot(2, 2, 1);
imshow(rgbImage, []);
axis on;
caption = sprintf('Original RGB Image\n%s', baseFileName);
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
hp = impixelinfo(); % Set up status line to see values when you mouse over the image.
% Set up figure properties:
% Enlarge figure to full screen.
g2 = gcf;
g2.WindowState = "maximized";
% 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')
% Segment the image.
[mask, maskedRGBImage] = createMask(rgbImage);
% Display image.
subplot(2, 2, 2);
imshow(mask, []);
axis on;
caption = sprintf('Color Segmentation Mask Image');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
hp = impixelinfo(); % Set up status line to see values when you mouse over the image.
% Clean mask.
% Fill black holes.
mask = imfill(mask, 'holes');
% Take the largest blob only
mask = bwareafilt(mask, 1);
% Do an opening to break off any little tendrils
diskRadius = 3;
se = strel('disk', diskRadius, 0);
mask = imopen(mask, se);
% Some tendrils may get disconnected. Take the largest blob only.
mask = bwareafilt(mask, 1);
% Do a closing to smooth out any little bays into the main objects.
mask = imclose(mask, se);
% Fill black holes.
mask = imfill(mask, 'holes');
% Display image.
subplot(2, 2, 3);
imshow(mask, []);
axis on;
caption = sprintf('Final Mask Image');
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
hp = impixelinfo(); % Set up status line to see values when you mouse over the image.
% Get the circularity and area.
props = regionprops(mask, 'Circularity', 'Area');
circ = props.Circularity;
% Plot the borders of all the blobs in the overlay above the original grayscale image
% using the coordinates returned by bwboundaries().
% bwboundaries() returns a cell array, where each cell contains the row/column coordinates for an object in the image.
% Display image.
subplot(2, 2, 4);
imshow(rgbImage, []);
axis on;
caption = sprintf('Original RGB Image\n%s', baseFileName);
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
hp = impixelinfo(); % Set up status line to see values when you mouse over the image.
% Here is where we actually get the boundaries for each blob.
boundaries = bwboundaries(mask);
% boundaries is a cell array - one cell for each blob.
% In each cell is an N-by-2 list of coordinates in a (row, column) format. Note: NOT (x,y).
% Column 1 is rows, or y. Column 2 is columns, or x.
numberOfBoundaries = size(boundaries, 1); % Count the boundaries so we can use it in our for loop
% Here is where we actually plot the boundaries of each blob in the overlay.
hold on; % Don't let boundaries blow away the displayed image.
for k = 1 : numberOfBoundaries
thisBoundary = boundaries{k}; % Get boundary for this specific blob.
x = thisBoundary(:,2); % Column 2 is the columns, which is x.
y = thisBoundary(:,1); % Column 1 is the rows, which is y.
plot(x, y, 'r-', 'LineWidth', 2); % Plot boundary in red.
hold off;
caption = sprintf('%d Outlines, from bwboundaries()', numberOfBoundaries);
fontSize = 15;
title(caption, 'FontSize', fontSize);
axis('on', 'image'); % Make sure image is not artificially stretched because of screen's aspect ratio.
message = sprintf('The area = %d and the circularity = %f', props.Area, circ);
fprintf('%s\n', message);
function [BW,maskedRGBImage] = createMask(RGB)
%createMask Threshold RGB image using auto-generated code from colorThresholder app.
% [BW,MASKEDRGBIMAGE] = createMask(RGB) thresholds image RGB using
% auto-generated code from the colorThresholder app. The colorspace and
% range for each channel of the colorspace were set within the app. The
% segmentation mask is returned in BW, and a composite of the mask and
% original RGB images is returned in maskedRGBImage.
% Auto-generated by colorThresholder app on 30-May-2023
% Convert RGB image to chosen color space
I = rgb2hsv(RGB);
% Define thresholds for channel 1 based on histogram settings
channel1Min = 0.957;
channel1Max = 0.038;
% Define thresholds for channel 2 based on histogram settings
channel2Min = 0.065;
channel2Max = 1.000;
% Define thresholds for channel 3 based on histogram settings
channel3Min = 0.870;
channel3Max = 1.000;
% Create mask based on chosen histogram thresholds
sliderBW = ( (I(:,:,1) >= channel1Min) | (I(:,:,1) <= channel1Max) ) & ...
(I(:,:,2) >= channel2Min ) & (I(:,:,2) <= channel2Max) & ...
(I(:,:,3) >= channel3Min ) & (I(:,:,3) <= channel3Max);
BW = sliderBW;
% Initialize output masked image based on input image.
maskedRGBImage = RGB;
% Set background pixels where BW is false to zero.
maskedRGBImage(repmat(~BW,[1 1 3])) = 0;

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