Minimum distance between two objects

@Image Analyst Hello, I tried to find the minimum distance bewteen two objects as per the code you provided on a similar post- 'test3.m' but I am unable to find the distance. Instead, I am able to find the distance from the boundary of the image to one of the objects. I need to find the minimum distance between the two bones, I am attaching two images, an input and an output image for your review, and I am also attaching the code for you took review, please help me find the minimum distance. Thank you.

Answer 1

[Matt J]

load('Bones')

reg=regionprops(Image,'PixelList');

[D,I1] = pdist2(reg.PixelList,'euclidean','Smallest',1);

[minDistance,I2]=min(D);
minDistance,

minDistance = 7.0711

imshow(Image)
hold on 
 xy=num2cell([reg(1).PixelList(I1(I2),:);reg(2).PixelList(I2,:)],1);
 line(xy{:},'Color','c','LineWidth',3);
 axis([113.0131  208.1735  186.9023  282.0627])
hold off

[Vihan P]

I am getting this erros, how can I resolve it? @Matt J

[Matt J]

There's no way to know without seeing the image.

[Matt J]

Also, please post code in a code well rather than as a screenshot (so it can be more easily copy/pasted).

[Vihan P]

I am unable to retrieve any image due to this error. I will paste my code here for your review, I apologize for the inconvenience @Matt J

function test4
A = imread('9002116_060_pred.png');
figure(1); imshow(A);

Imm= imfill(A, 'holes');
figure(2); imshow(Imm);

%load('Bones')

reg=regionprops(A,'PixelList');

[D,I1] = pdist2(reg.PixelList,'euclidean','Smallest',1);

[minDistance,I2]=min(D);
minDistance,

imshow(A)
hold on 
 xy=num2cell([reg(1).PixelList(I1(I2),:);reg(2).PixelList(I2,:)],1);
 line(xy{:},'Color','c','LineWidth',3);
 axis([113.0131  208.1735  186.9023  282.0627])
hold off
end

[Matt J]

You need to attach '9002116_060_pred.png'

Obviously the code works on the previous image that you posted, as I have demonstrated.

[Vihan P]

Here is the image @Matt J:

Also, I need to make this work for 200 such images. How can I go about it? Your guidance will be much appreciated. @Matt J

[Matt J]

A=imbinarize(imread('9002116_060_pred.png'));

reg=regionprops(A,'PixelList');
[D,I1] = pdist2(reg.PixelList,'euclidean','Smallest',1);
[minDistance,I2]=min(D);
minDistance,

minDistance = 7.0711

imshow(A)
hold on 
 xy=num2cell([reg(1).PixelList(I1(I2),:);reg(2).PixelList(I2,:)],1);
 line(xy{:},'Color','c','LineWidth',3);
 axis([113.0131  208.1735  186.9023  282.0627])
hold off

[Vihan P]

@Matt J I got it! Thank you so much. Just wanted to let you know that I have to come up with a solution that can work for different bone sizes and distances. I have about 200 slices of such bones, each varying in size and distance. How can I automate this code for all 200 slices?

[Vihan P]

@Matt J I basically need to do the following:

1. Calculate the minimum distance between two bones.
2. Automate the code for 200 such bone samples (each varying in sizes and distances) to find their minimum distance.
3. Put all the minimum distances in a vector.

How can I automate the code that you have helped me with to achieve the above?

Thank you.

Answer 2

[Image Analyst]

@Vihan P, see code adapted for your image. I also did it both ways, using sqrt(), where I just plotted one of the closest pairs, and pdist2() where I plotted all 3 of the closest pairs. If it helps you, please vote for the answer. I also answered you in your original "Answer" in the other person's thread where you first asked 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;
fprintf('Beginning to run %s.m ...\n', mfilename);

fontSize = 13;

% 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 standard MATLAB color demo image.
folder = pwd;  %'C:\Users\Lakshya\Documents\Temporary';
baseFileName = 'knee.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);
% Display the original image.
subplot(2, 2, 1);
imshow(grayImage);
axis on;
title('Original Gray Scale Image', 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);

% Binarize the image
binaryImage = imbinarize(grayImage);
% Display the image.
subplot(2, 2, 2);
imshow(binaryImage);
title('Binary Image', 'FontSize', fontSize);

% Fill the outline to make it solid so we don't get boundaries
% on both the inside of the shape and the outside of the shape.
binaryImage = imfill(binaryImage, 'holes');
% Display the image.
subplot(2, 2, 3);
imshow(binaryImage);

% 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.
hold on;
boundaries = bwboundaries(binaryImage);
numberOfBoundaries = size(boundaries, 1);
for k = 1 : numberOfBoundaries
	thisBoundary = boundaries{k};
	plot(thisBoundary(:,2), thisBoundary(:,1), 'r', 'LineWidth', 3);
end
title('Filled Binary Image with Boundaries', 'FontSize', fontSize);
hold off;

% Define object boundaries
numberOfBoundaries = size(boundaries, 1)
boundary1 = boundaries{1};
boundary2 = boundaries{2};
boundary1x = boundary1(:, 2);
boundary1y = boundary1(:, 1);
x1=1;
y1=1;
x2=1;
y2=1;
overallMinDistance = inf; % Initialize.
index1 = 1;
index2 = 1;
for k = 1 : length(boundary2)
    boundary2x = boundary2(k, 2);
    boundary2y = boundary2(k, 1);	
    % For this blob, compute distances from boundaries to edge.
    allDistances = sqrt((boundary1x - boundary2x).^2 + (boundary1y - boundary2y).^2);
    % Find closest point, min distance.
    [minDistance(k), indexOfMin] = min(allDistances);
	if minDistance(k) < overallMinDistance
		overallMinDistance = minDistance(k);
		x1 = boundary1x(indexOfMin);
		y1 = boundary1y(indexOfMin);
		x2 = boundary2x;
		y2 = boundary2y;
		index2 = k;
		index1 = indexOfMin;
	end
end
% Report to command window.
fprintf('Min Distance from sqrt() method = %f at index %d of boundary 1 and index %d of boundary 2.\n', ...
	overallMinDistance, index1, index2);

hFig = figure;
h1 = subplot(1, 2, 1);
imshow(binaryImage);
axis on;
title('Closest Distance from sqrt()', 'FontSize', fontSize);
h2 = subplot(1, 2, 2);
imshow(binaryImage);
axis on;
title('Closest Distances from pdist2()', 'FontSize', fontSize);
hFig.WindowState = 'maximized';
hold on;
% Draw a line between point 1 and 2
line(h1, [x1, x2], [y1, y2], 'Color', 'y', 'LineWidth', 3);

%======================================================================================
% For comparison, use pdist2()
allDistances2 = pdist2(boundary1, boundary2);
minDistance2 = min(allDistances2(:));
% Find all points that have that min distance - there may be several that have it.
[r, c] = find(allDistances2 == minDistance2)
boundary1x = boundary1(:, 2);
boundary1y = boundary1(:, 1);
boundary2x = boundary2(:, 2);
boundary2y = boundary2(:, 1);
for k = 1 : length(r)
	% Report to command window.
	index1 = r(k);
	index2 = c(k);
	fprintf('Min Distance from pdist2() method = %f at index %d of boundary 1 and index %d of boundary 2.\n', ...
		minDistance2, index1, index2);
	xLine = [boundary1x(index1), boundary2x(index2)];
	yLine = [boundary1y(index1), boundary2y(index2)];
	line(h2, xLine, yLine, 'Color', 'm', 'LineWidth', 1.5);
end

You can't see the magenta lines in the right image very well, but they're there.

[Vihan P]

@Image Analyst Thank you for the instant reply on the message. Your code worked efficiently, also I am able to view the minimum distance lines clearly.

I have a folder or 200 patients, each patient having approximately 150 such bones samples.

I want to apply this algorithm for every patient having 150 bone samples(each varying in sizes and distances).

For every patient, I want to find the average minimum distance by taking an average of these 150 bone samples and store the result in a vector. I want to do the same for all 200 patients.

How can I automate the process for all the patients? Please help me understand this.

Thank you.

[Image Analyst]

@Vihan P, see the FAQ for code samples:

https://matlab.fandom.com/wiki/FAQ#How_can_I_process_a_sequence_of_files.3F

[Vihan P]

I tried to jot three pieces together: 1. That loads multiple images from the location of my folder, 2. A procedure that finds the minimum distance between two bones for 1 sample(image), 3. Took a sample set out of the whole dataset, and apply the minimum distance from the previous step to all the samples(images). I keep getting the error: Index exceeds the number of array elements (1). I tried to fix the code in multiple ways, but I couldn't find an appropriate solution, I dont know where am I going wrong with the code. Please help me debug this code and run it for multiple images. I am attaching the code sample and a sample dataset along with it for you to review. Thank you.

@Image Analyst

function test10
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
myFolder = 'D:\MathWorks_MATLAB_R2020a_v9.8.0.1323502\MathWorks_MATLAB_R2020a_v9.8.0.1323502\pq';
% Check to make sure that folder actually exists.  Warn user if it doesn't.
if ~isfolder(myFolder)
    errorMessage = sprintf('Error: The following folder does not exist:\n%s\nPlease specify a new folder.', myFolder);
    uiwait(warndlg(errorMessage));
    myFolder = uigetdir(); % Ask for a new one.
    if myFolder == 0
         % User clicked Cancel
         return;
    end
end
% Get a list of all files in the folder with the desired file name pattern.
filePattern = fullfile(myFolder, '*.png'); % Change to whatever pattern you need.
theFiles = dir(filePattern);
for k = 1 : length(theFiles)
    baseFileName = theFiles(k).name;
    fullFileName = fullfile(theFiles(k).folder, baseFileName);
    fprintf(1, 'Now reading %s\n', fullFileName);
    % Now do whatever you want with this file name,
    % such as reading it in as an image array with imread()
    imageArray = imread(fullFileName);
    %imshow(imageArray);  % Display image.
    %drawnow; % Force display to update immediately.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Get the dimensions of the image.  numberOfColorBands should be = 3.
[rows, columns, numberOfColorBands] = size(imageArray);
% Display the original image.
subplot(2, 2, 1);
imshow(imageArray);
axis on;
title('Original Gray Scale Image');
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);

% Binarize the image
binaryImage = imbinarize(imageArray);
% Display the image.
subplot(2, 2, 2);
imshow(binaryImage);
title('Binary Image');

% Fill the outline to make it solid so we don't get boundaries
% on both the inside of the shape and the outside of the shape.
binaryImage = imfill(binaryImage, 'holes');
% Display the image.
subplot(2, 2, 3);
imshow(binaryImage);

% 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.
hold on;
boundaries = bwboundaries(binaryImage);
numberOfBoundaries = size(boundaries, 1);
for k = 1 : numberOfBoundaries
	thisBoundary = boundaries{k};
	plot(thisBoundary(:,2), thisBoundary(:,1), 'r', 'LineWidth', 3);
end
title('Filled Binary Image with Boundaries');
hold off;

% Define object boundaries
numberOfBoundaries = size(boundaries, 1)
boundary1 = boundaries{1};
boundary2 = boundaries{2};
boundary1x = boundary1(:, 2);
boundary1y = boundary1(:, 1);
x1=1;
y1=1;
x2=1;
y2=1;
overallMinDistance = inf; % Initialize.
index1 = 1;
index2 = 1;
for k = 1 : length(boundary2)
    boundary2x = boundary2(k, 2);
    boundary2y = boundary2(k, 1);	
    % For this blob, compute distances from boundaries to edge.
    allDistances = sqrt((boundary1x - boundary2x).^2 + (boundary1y - boundary2y).^2);
    % Find closest point, min distance.
    [minDistance(k), indexOfMin] = min(allDistances);
	if minDistance(k) < overallMinDistance
		overallMinDistance = minDistance(k);
		x1 = boundary1x(indexOfMin);
		y1 = boundary1y(indexOfMin);
		x2 = boundary2x;
		y2 = boundary2y;
		index2 = k;
		index1 = indexOfMin;
	end
end
% Report to command window.
fprintf('Min Distance from sqrt() method = %f at index %d of boundary 1 and index %d of boundary 2.\n', ...
	overallMinDistance, index1, index2);

hFig = figure;
h1 = subplot(1, 2, 1);
imshow(binaryImage);
axis on;
title('Closest Distance from sqrt()');
h2 = subplot(1, 2, 2);
imshow(binaryImage);
axis on;
title('Closest Distances from pdist2()');
hFig.WindowState = 'maximized';
hold on;
% Draw a line between point 1 and 2
line(h1, [x1, x2], [y1, y2], 'Color', 'y', 'LineWidth', 3);

%======================================================================================
% For comparison, use pdist2()
allDistances2 = pdist2(boundary1, boundary2);
minDistance2 = min(allDistances2(:));
% Find all points that have that min distance - there may be several that have it.
[r, c] = find(allDistances2 == minDistance2)
boundary1x = boundary1(:, 2);
boundary1y = boundary1(:, 1);
boundary2x = boundary2(:, 2);
boundary2y = boundary2(:, 1);
for k = 1 : length(r)
	% Report to command window.
	index1 = r(k);
	index2 = c(k);
	fprintf('Min Distance from pdist2() method = %f at index %d of boundary 1 and index %d of boundary 2.\n', ...
		minDistance2, index1, index2);
	xLine = [boundary1x(index1), boundary2x(index2)];
	yLine = [boundary1y(index1), boundary2y(index2)];
	line(h2, xLine, yLine, 'Color', 'm', 'LineWidth', 1.5);
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
bones = 84; % Number of images you have for each person
people = 5; % Number of people you have

% Preallocate array for each person's minimum distance
personal_min_dist = zeros(people,1);

% Preallocate array for each bone's minimum distance
% This assumes that you have the exact same number of
% images for each person. If not, this array may need
% to change size inside the loop
bones_min_dist = zeros(bones,1) 

for person = 1:people
    % Get the set of images for each person
    personal_bones_data = imageArray(person) 
    % Your actual setup here will depend on how your data is stored
    
    for image = 1:bones
        % Get minimum distance for each image using ImageAnalyst's function
        bones_min_dist(image) = minDistance2(personal_bones_data(image))
    end
   % Get bone distance as mean of all distances for a person
   personal_bone_dist(person) = mean(bones_min_dist); 
    
end

end