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

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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.
Zara Khan
Zara Khan on 3 Mar 2020
I have already used regionprops and i have got the centroid too. Now what next ???I have attached image

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Accepted Answer

Image Analyst
Image Analyst on 3 Mar 2020
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);
% 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);
% 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.
% 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.
% Binarize the image
mask = ~imbinarize(grayImage);
title('Mask Image', 'FontSize', fontSize, 'Interpreter', 'None');
hFig.WindowState = 'maximized'; % May not work in earlier versions of MATLAB.
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;
% 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.
Image Analyst
Image Analyst on 4 Mar 2020
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.

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