How can I calculate centroid to contour distance for an binary image every after 10 degree?

After finding out the centroid I want to draw lines from centroid to contour every after 10 degree. Then want to calculate the distances from centoird to contour and stored it in an array.

[Hank]

Can you share what you've tried?

The regionprops function can give you the centroid of objects in a binary image.

im = imread('image.png');
blob = regionprops(im,'Centroid') % run region props on im, requesting centroid of blobs

imshow(im);
plot(blob.centroid(1),blob.centroid(2),'ro')

[Zara Khan]

I have already used regionprops and i have got the centroid too. Now what next ???I have attached image

Answer 1

[Image Analyst]

Try this code:

% Initialization steps.
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 = 20;

%===============================================================================
% Read in demo image.
folder = pwd;
baseFileName = 'is this your image.jpg';
% 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
% Read in the image from disk.  If storedColorMap is not empty, it's an indexed image with a stored colormap.
[grayImage, storedColorMap] = imread(fullFileName);
if ~isempty(storedColorMap)
	grayImage = ind2rgb(grayImage, storedColorMap);
end
% 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.
hFig = figure;
imshow(grayImage, []);
title('Original Grayscale Image', 'FontSize', fontSize, 'Interpreter', 'None');
hFig.WindowState = 'maximized'; % May not work in earlier versions of MATLAB.
drawnow;

% Binarize the image
mask = ~imbinarize(grayImage);
imshow(mask);
title('Mask Image', 'FontSize', fontSize, 'Interpreter', 'None');
hFig.WindowState = 'maximized'; % May not work in earlier versions of MATLAB.
drawnow;

mask = bwareafilt(mask, 1); % Extract largest blob (in case there is more than 1).
props = regionprops(mask, 'Centroid');
xCenter = props.Centroid(1);
yCenter = props.Centroid(2);
boundary = bwboundaries(mask);
boundary = boundary{1};
x = boundary(:, 2);
y = boundary(:, 1);
% Compute all distances from centroid to every boundary point:
distances = sqrt((x - xCenter) .^ 2 + (y - yCenter) .^ 2);
fprintf('The mean distance from centroid is %.2f pixels.\n', mean(distances));
% Compute angles.  They go from -180 to +180.
angles = atan2d(y - yCenter, x - xCenter);
% Find out which one is closest to every 10 degrees.
hold on;
counter = 1;
for angle = -180 : 10 : 170
	angleDifference = abs(angles - angle);
	[minAngle, index] = min(angleDifference);
	% Draw a line from the centroid to that index.
	line([x(index), xCenter], [y(index), yCenter], 'Color', 'r', 'LineWidth', 2);
	indexesAtDeltaAngles(counter) = index;
	counter = counter + 1;
end
% Get the mean of the indexes that belong to the "every 10 degrees" subset (if you want that).
fprintf('The mean distance from centroid at those specific angles is %.2f pixels.\n', mean(distances(indexesAtDeltaAngles)));

You'll see

The mean distance from centroid is 82.25 pixels.

The mean distance from centroid at those specific angles is 73.72 pixels.

Not sure why you're doing just a few angles since it's much easier to just compute the angles and distances of every single boundary point.

[Zara Khan]

Image Analyst: Thank you. After considering angles after every 10 degree You have plotted lines from centroid to contour. Can we store those particulars line distance (centroid to contour) to an array ? here disntances from centroid to all boundaries points have been considered ,this is ok. But I too want those particulars line distances which are drawn after every 10 degree.

Yes I already have computed the angles and distances of every single boundary point. Just trying new things.

[Image Analyst]

If you looked at the last line, you can see that they are in distances(indexesAtDeltaAngles). To extract into a brand new variable, just do

distances10 = distances(indexesAtDeltaAngles);

Like I said, using just this subset will result in worse, coarser data and I don't recommend doing it.