How to measure crack width

I noticed that when I reduced the minimum acceptable area the crack width decreased although the appearance of blobs in this situation, while when i increased minimum acceptable area and the blobs removed and the width increased

for example:

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 = 10;
% Read in image and convert to gray scale.
rgbImage = imread('7004-46.jpg');
I = rgb2gray(rgbImage);
subplot(2,2,1);
imshow(I);
title('7004-46');
% Take histogram.
subplot(2,2,2);
histogram(I);
grid on;  %to make grid line
title('histogram Gray image');
% Binarize image.
threshold = 145;
xline(threshold, 'Color', 'r', 'LineWidth',2)
mask=I < threshold;
mask = imfill(mask, 'holes');
% Filter image.
se = strel('line',1, 0);
filteredImage = imclose(mask, se);
subplot(2, 2, 3);
imshow(filteredImage);
title('closed Image');
%%% Measure the areas to determin minAcceptableArea show the histogram of
%%% area
% props = regionprops(filteredImage, 'Area');
% allAreas = sort([props.Area], 'descend');
% histogram(allAreas)
%%Look at the histogram.  What area do you think is the minimum size to be a valid crack?
minAcceptableArea =20;
mask = bwareafilt(filteredImage, [minAcceptableArea, inf]);
subplot(2, 2, 4);
imshow(mask);
% 
% % Measure size of crack.
props = regionprops(mask, 'Area');
allAreas = [props.Area];
out=bwferet(mask)
% Measure the areas to know the area not to be considered
% props = regionprops(mask, 'Area');
% allAreas = sort([props.Area], 'descend')
% Get width = area / maximum length
averageWidths = sort(allAreas ./ out.MaxDiameter, 'descend');
message = sprintf('The average width = %.2f pixels.', averageWidths(1));
fprintf('%s\n', message);
caption = sprintf('Binary Image of Cracks larger than %d\nAverage width of largest = %.2f pixels', minAcceptableArea, averageWidths(1));
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
uiwait(helpdlg(message));

[yasmin ismail]

another thing when i added the whole areas and divided by the maximum diamter I got different answer for example if minimum accpetable area is 100 the output is :

3×3 table

MaxDiameter MaxAngle MaxCoordinates

________________ _________________ ______________

136.297468795279 -108.833752912659 {2×2 double}

45.6946386351835 113.198590513648 {2×2 double}

69.3541635375988 -95.7927964950321 {2×2 double}

allAreas =

523 253 230

The average width = 11.45 pixels.

if I take (523+253+230)/136.29= 7.46 not 11.45

[yasmin ismail]

@Image Analyst

Answer 1

[Image Analyst]

That doesn't look like the right algorithm if you want to find crack widths. Did you see my post where I found the mean width of a blob (image of lightning)? Basically you want to

1. Threshold/mask your image
2. Get the Euclidean Distance Transform (EDT) of your mask
3. Get the skeleton of your mask
4. Multiply your EDT image by your skeleton image to get the radius of the blob at each point along it's skeleton;
5. Double the radii to get the widths.
6. Display the histogram to see the distribution, or take the mean.

See attached demo.

[yasmin ismail]

@Image Analyst In the demo you didnt mentioned the value of the threshold .

I used your demo with another image but I didnt get the answer and I dont know where the values must be changed to let this demo be matched with other images

for example the attached image

[Image Analyst]

Try this, but first download my interactive thresholding app:

https://www.mathworks.com/matlabcentral/fileexchange/29372-thresholding-an-image?s_tid=srchtitle

% Program to compute the mean width of a blob in an image.
clearvars;
close all;
clc;
fontSize = 15;
% Read in original image, with white lightning on black background.
baseFileName = '7004-46.jpg';
fullFileName = fullfile(pwd, baseFileName);
grayImage = imread(fullFileName);
% 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(rgbImage);
    % 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.
else
    grayImage = grayImage; % It's already gray scale.
end
% Now it's gray scale with range of 0 to 255.
subplot(2, 3, 1);
imshow(grayImage, [])
impixelinfo; % Let user mouse around and see values in the status line at the lower right.
title('Original Image', 'FontSize', fontSize);
% Binarize the image.
% mask = imbinarize(grayImage);
lowThreshold = 0;
highThreshold = 160;

% Interactively and visually set a threshold on a gray scale image.
% https://www.mathworks.com/matlabcentral/fileexchange/29372-thresholding-an-image?s_tid=srchtitle
[lowThreshold, highThreshold] = threshold(lowThreshold, highThreshold, grayImage);
mask = grayImage >= lowThreshold & grayImage <= highThreshold;
% Fill holes.
mask = imfill(mask, 'holes');
% Take largest blob only.
mask = bwareafilt(mask, 1);
subplot(2, 3, 2);
imshow(mask)
impixelinfo; % Let user mouse around and see values in the status line at the lower right.
title('Mask', 'FontSize', fontSize)
% Compute the skeleton
skelImage = bwskel(mask);
subplot(2, 3, 3);
imshow(skelImage)
impixelinfo; % Let user mouse around and see values in the status line at the lower right.
title('Thinned', 'FontSize', fontSize)
% Enlarge figure to full screen.
g = gcf;
g.WindowState = 'maximized';
% Compute the Euclidean distance image.
edtImage = bwdist(~mask);
subplot(2, 3, 4);
imshow(edtImage, [])
title('Distance Transform Image', 'FontSize', fontSize);
impixelinfo; % Let user mouse around and see values in the status line at the lower right.
% Multiply them to get an image where the only pixels in the image
% are along the skeleton and their value is the radius.
% Multiply radius image by 2 to get diameter image.
diameterImage = 2 * edtImage .* single(skelImage);
subplot(2, 3, 5);
imshow(diameterImage, [])
title('Diameter Image', 'FontSize', fontSize);
impixelinfo; % Let user mouse around and see values in the status line at the lower right.
% Get the widths.  These will be where the image is not zero.
widths = diameterImage(diameterImage > 0);
% Show histogram of widths.
subplot(2, 3, 6);
histogram(widths);
grid on;
xlabel('Width in Pixels', 'FontSize', fontSize);
ylabel('Count', 'FontSize', fontSize);
% Compute the mean width
meanWidth = mean(widths)
% Put a line on the histogram at the mean width
xline(meanWidth, 'LineWidth', 2, 'Color', 'r');
caption = sprintf('Histogram of Widths.  Mean Width = %.1f Pixels', meanWidth);
title(caption, 'FontSize', fontSize);
message = sprintf('Mean Width = %.1f Pixels', meanWidth);
msgbox(message);

[yasmin ismail]

What I understood that the demo of threshold will automatically show the min and mx threshold from threshold interface right?

what about option of otsu ,shall i use it or no need?

[Image Analyst]

Yes, I used my utility to determine the threshold. You might be able to use an automatic way like graythresh or otsuthresh, or my attached triangle threshold.

[yasmin ismail]

when i used traingle threshold I got warning dialog:

Undefined function'triangle-thrshold' for input arrgument of type 'double'

[Image Analyst]

You can also use the triangle threshold in the interactive thresholding app I attached. It's a button in the lower right of the window.

I'm pretty sure triangle threshold works for double inputs (the counts array from the histogram are always double I believe). You'd need to attach your script that calls the triangle threshold function so I can see what's wrong.

[yasmin ismail]

1-I uploaded the image that i got warning function of triangle thresholding

2-The other thing I noticed , that if i used automatic threshold in the result image I attached for the width, you will find that part of crack not considered at the left side in (diameter image) not whole cracked appear?

3- also in width histogram the average width is 3.8 although its zero frequency in histogram, so why it is not 4 which is at middle and highest frequency?

[yasmin ismail]

I have also a question, how can I get the maximum width instead of mean?

@Image Analyst

[Image Analyst]

maxWidths = max(widths)

[yasmin ismail]

I am confusing when choosing the threshold for example the image i attached:

when I run automatically the interface show min and max threshold 79 and 160, when I click ok the result shown as i attached and you will find that part of crack wont apear as I marked in the attached image and maybe this part include max width, so can you clarify this point and how to fix it to let all crack appear.

Thanks alot for your help

I attached the original image and the result of the run

@Image Analyst

[yasmin ismail]

@Image Analyst

I am confusing when choosing the threshold for example the image i attached:

when I run automatically the interface show min and max threshold 79 and 160, when I click ok the result shown as i attached and you will find that part of crack wont apear as I marked in the attached image and maybe this part include max width, so can you clarify this point and how to fix it to let all crack appear.

Thanks alot for your help

I attached the original image and the result of the run

[Image Analyst]

You need to take out the call to bwareafilt() where it extracts only the largest skeleton and use bwareaopen

% Program to compute the mean width of a blob in an image.
clearvars;
close all;
clc;
fontSize = 15;
% Read in original image, with white lightning on black background.
baseFileName = '7049-198.jpg';
fullFileName = fullfile(pwd, baseFileName);
grayImage = imread(fullFileName);
% 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(rgbImage);
    % 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.
else
    grayImage = grayImage; % It's already gray scale.
end
% Now it's gray scale with range of 0 to 255.
subplot(2, 3, 1);
imshow(grayImage, [])
impixelinfo; % Let user mouse around and see values in the status line at the lower right.
title('Original Image', 'FontSize', fontSize);
% Binarize the image.
% mask = imbinarize(grayImage);
lowThreshold = 0;
highThreshold = 160;
% Interactively and visually set a threshold on a gray scale image.
% https://www.mathworks.com/matlabcentral/fileexchange/29372-thresholding-an-image?s_tid=srchtitle
[lowThreshold, highThreshold] = threshold(lowThreshold, highThreshold, grayImage);
mask = grayImage >= lowThreshold & grayImage <= highThreshold;
% Fill holes.
mask = imfill(mask, 'holes');
subplot(2, 3, 2);
imshow(mask)
impixelinfo; % Let user mouse around and see values in the status line at the lower right.
title('Mask', 'FontSize', fontSize)

% Take largest blob only.
% mask = bwareafilt(mask, 1);
% Compute the skeleton
skelImage = bwskel(mask);
% Find the areas of all the skeletons.
props = regionprops(skelImage, 'Area');
allAreas = sort([props.Area])
% Extract only skeletons longer than 60 pixels.
skelImage = bwareaopen(skelImage, 60);
subplot(2, 3, 3);
imshow(skelImage)
impixelinfo; % Let user mouse around and see values in the status line at the lower right.
title('Thinned', 'FontSize', fontSize)
% Enlarge figure to full screen.
g = gcf;
g.WindowState = 'maximized';

% Compute the Euclidean distance image.
edtImage = bwdist(~mask);
subplot(2, 3, 4);
imshow(edtImage, [])
title('Distance Transform Image', 'FontSize', fontSize);
impixelinfo; % Let user mouse around and see values in the status line at the lower right.

% Multiply them to get an image where the only pixels in the image
% are along the skeleton and their value is the radius.
% Multiply radius image by 2 to get diameter image.
diameterImage = 2 * edtImage .* single(skelImage);
subplot(2, 3, 5);
imshow(diameterImage, [])
title('Diameter Image', 'FontSize', fontSize);
impixelinfo; % Let user mouse around and see values in the status line at the lower right.

% Get the widths.  These will be where the image is not zero.
widths = diameterImage(diameterImage > 0);
% Show histogram of widths.
subplot(2, 3, 6);
histogram(widths);
grid on;
xlabel('Width in Pixels', 'FontSize', fontSize);
ylabel('Count', 'FontSize', fontSize);
% Compute the mean width
meanWidth = mean(widths)
% Put a line on the histogram at the mean width
xline(meanWidth, 'LineWidth', 2, 'Color', 'r');
caption = sprintf('Histogram of Widths.  Mean Width = %.1f Pixels', meanWidth);
title(caption, 'FontSize', fontSize);
message = sprintf('Mean Width = %.1f Pixels', meanWidth);
msgbox(message);

[yasmin ismail]

But how did you choose 60 ?

% Extract only skeletons longer than 60 pixels.

skelImage = bwareaopen(skelImage, 60);

@Image Analyst

[Image Analyst]

I just picked it. There is lots of noise in the image. There are cracks of all different lengths from just a few pixels to thousands of pixels. What is YOUR definition of how long a legitimate crack should be? Do you want cracks just 4 pixels long? A hundred long? It's very complicated. There is no one single overall, grand "right" answer for the minimum size of valid cracks, so you just have to define one. Do you care about analyzing a crack if it's only 2 pixels long? I think not. So what is your minimum length that you care about?

[yasmin ismail]

Thanks alot @Image Analyst

[yasmin ismail]

@Image Analyst

I have question about determining threshold:

In the inteface of threshold there are three options to determine it ;

1)otsu

2)triangle

3)manually by detriming the lower and upper threshold

My question is what you call option No.3, is it greythresh or what you call it exactly?

[Image Analyst]

I call it "manual" because the thresholds are what you set by moving the sliders (scrollbars). The threshold is not selected by an automatic method such as Otsu or triangle or graythresh, it's selected by you manually.

[yasmin ismail]

@Image Analyst

what do you mean by

impixelinfo; % Let user mouse around and see values in the status line at the lower right.

which values you mean?

[Image Analyst]

The (x,y) location and RGB color values of the pixel underneath your cursor.

[yasmin ismail]

yes I got it.

But why you asked for it twice, for diameter image and distance transform image, wht is the important to see thier values in both?

[Image Analyst]

I never know where or when I might need it so I usually just call impixelinfo as a matter of habit after I call imshow(). Better to have it if you need it than to not have it. It doesn't hurt to have it, so why not? If you're sure you'll never need it, you can delete that line of code.

[yasmin ismail]

@Image Analyst thnx lot for your clarification

[yasmin ismail]

@Image Analyst How can I measure the following:

1- length of crack and

2-its angle at different points

[Image Analyst]

1. Sum up the skeleton image (will count all cracks in the image if there is more than one). This will give you a pixel count.
2. Not sure what you mean but atand or atan2d will give you angles, given a delta y and delta x.

Answer 2

[yasmin ismail]

@Image Analyst another question:

To find width,if I used (bwperim) instead of (bwskel) will be more accurate?

[yasmin ismail]

@Image Analyst

I am trying to find max width of this image which includes many cracks but I have difficulities with determing thresholding with same above technique

thus, can you help me to fix it please?

[Image Analyst]

Try adapthisteq to flatten the image so that you can use a global threshold. Or use imbinarize with the locally adaptive option. Once you have the binary image, you can take the max of the Euclidean Distance Transform to find the radius of the max crack or blob.

[yasmin ismail]

@Image Analyst

I did both option

for first option using adapthisteq I used following code and the result is attached image called (adapthistic)

I = imread('real2.jpg');

II=rgb2gray(I);

J = adapthisteq(II);

level = graythresh(J);

imshowpair(II,J,'montage')

for second option the result also attached called (imbinarize with local adaptive) , the following code:

T=imread('real2.jpg');

T2=rgb2gray(T);

BW2=imbinarize(T2,'adaptive');

imshowpair(T2,BW2,'montage')

are that what do you mean?

[Image Analyst]

Yeah, basically, though you may need to change some of the parameters, like the threshold level or, in the case of adapthisteq, the tile size.

[yasmin ismail]

@Image Analyst

I tried to find mean and max width fo attached image i tried many options but i couldnt fix it, can you help me please

% Program to compute the mean width of a blob in an image.

clearvars;

close all;

clc;

fontSize = 15;

% Read in original image, with white lightning on black background.

baseFileName = '44.jpg';

fullFileName = fullfile(pwd, baseFileName);

grayImage = imread(fullFileName);

% 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(rgbImage);

% 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.

else

grayImage = grayImage; % It's already gray scale.

end

% Now it's gray scale with range of 0 to 255.

subplot(2, 3, 1);

imshow(grayImage, [])

impixelinfo; % Let user mouse around and see values in the status line at the lower right.

title('Original Image', 'FontSize', fontSize);

% Binarize the image.

% mask = imbinarize(grayImage);

lowThreshold = 0;

highThreshold = 160;

% Interactively and visually set a threshold on a gray scale image.

% https://www.mathworks.com/matlabcentral/fileexchange/29372-thresholding-an-image?s_tid=srchtitle

[lowThreshold, highThreshold] = threshold(lowThreshold, highThreshold, grayImage);

mask = grayImage >= lowThreshold & grayImage <= highThreshold;

% Fill holes.

mask = imfill(mask, 'holes');

subplot(2, 3, 2);

imshow(mask)

impixelinfo; % Let user mouse around and see values in the status line at the lower right.

title('Mask', 'FontSize', fontSize)

% Take largest blob only.

% mask = bwareafilt(mask, 1);

% Compute the skeleton

skelImage = bwskel(mask);

% Find the areas of all the skeletons.

props = regionprops(skelImage, 'Area');

allAreas = sort([props.Area])

% Extract only skeletons longer than 60 pixels.

skelImage = bwareaopen(skelImage, 60);

subplot(2, 3, 3);

imshow(skelImage)

impixelinfo; % Let user mouse around and see values in the status line at the lower right.

title('Thinned', 'FontSize', fontSize)

% Enlarge figure to full screen.

g = gcf;

g.WindowState = 'maximized';

% Compute the Euclidean distance image.

edtImage = bwdist(~mask);

subplot(2, 3, 4);

imshow(edtImage, [])

title('Distance Transform Image', 'FontSize', fontSize);

impixelinfo; % Let user mouse around and see values in the status line at the lower right.

% Multiply them to get an image where the only pixels in the image

% are along the skeleton and their value is the radius.

% Multiply radius image by 2 to get diameter image.

diameterImage = 2 * edtImage .* single(skelImage);

subplot(2, 3, 5);

imshow(diameterImage, [])

title('Diameter Image', 'FontSize', fontSize);

impixelinfo; % Let user mouse around and see values in the status line at the lower right.

% Get the widths. These will be where the image is not zero.

widths = diameterImage(diameterImage > 0);

% Show histogram of widths.

subplot(2, 3, 6);

histogram(widths);

grid on;

xlabel('Width in Pixels', 'FontSize', fontSize);

ylabel('Count', 'FontSize', fontSize);

% Compute the mean width

meanWidth = mean(widths)

% Put a line on the histogram at the mean width

xline(meanWidth, 'LineWidth', 2, 'Color', 'r');

caption = sprintf('Histogram of Widths. Mean Width = %.1f Pixels', meanWidth);

title(caption, 'FontSize', fontSize);

message = sprintf('Mean Width = %.1f Pixels', meanWidth);

msgbox(message);

[Image Analyst]

That is a pretty variable image. Could you try a Hessian, Frangi, or COSFIRE-B filter on it? There are ones in the File Exchange.

[yasmin ismail]

@Image Analyst so, what you mean is I have to use filteration function first then saved image after filtrarion then I can applied above codes to get width,right?

[Image Analyst]

Yes, try various spatial filters so that you can get as much contrast between the crack and the background, and have the background be fairly flat (same gray level range). Then you can apply a fixed global threshold to discriminate crack from background.

Answer 3

[yasmin ismail]

@Image Analyst

I used filters as you advised me before and I could determine the threshold as shown in attached image but still the diameter image and distance transform the lines of crack is too small not clear enough, although I reduced (skeletons longer than 1 pixels)

but still not clear , can you help to fix it please

% Program to compute the mean width of a blob in an image.

clearvars;

close all;

clc;

fontSize = 15;

% Read in original image, with white lightning on black background.

baseFileName = 'A2.jpg';

fullFileName = fullfile(pwd, baseFileName);

grayImage = imread(fullFileName);

% 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(rgbImage);

% 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.

else

grayImage = grayImage; % It's already gray scale.

end

% Now it's gray scale with range of 0 to 255.

subplot(2, 3, 1);

imshow(grayImage, [])

impixelinfo; % Let user mouse around and see values in the status line at the lower right.

title('Original Image', 'FontSize', fontSize);

% Binarize the image.

% mask = imbinarize(grayImage);

lowThreshold = 0;

highThreshold = 40;

% Interactively and visually set a threshold on a gray scale image.

% https://www.mathworks.com/matlabcentral/fileexchange/29372-thresholding-an-image?s_tid=srchtitle

[lowThreshold, highThreshold] = threshold(lowThreshold, highThreshold, grayImage);

mask = grayImage >= lowThreshold & grayImage <= highThreshold;

% Fill holes.

mask = imfill(mask, 'holes');

subplot(2, 3, 2);

imshow(mask)

impixelinfo; % Let user mouse around and see values in the status line at the lower right.

title('Mask', 'FontSize', fontSize)

% Take largest blob only.

% mask = bwareafilt(mask, 1);

% Compute the skeleton

skelImage = bwskel(mask);

% Find the areas of all the skeletons.

props = regionprops(skelImage, 'Area');

allAreas = sort([props.Area])

% Extract only skeletons longer than 60 pixels.

skelImage = bwareaopen(skelImage, 1);

subplot(2, 3, 3);

imshow(skelImage)

impixelinfo; % Let user mouse around and see values in the status line at the lower right.

title('Thinned', 'FontSize', fontSize)

% Enlarge figure to full screen.

g = gcf;

g.WindowState = 'maximized';

% Compute the Euclidean distance image.

edtImage = bwdist(~mask);

subplot(2, 3, 4);

imshow(edtImage, [])

title('Distance Transform Image', 'FontSize', fontSize);

impixelinfo; % Let user mouse around and see values in the status line at the lower right.

% Multiply them to get an image where the only pixels in the image

% are along the skeleton and their value is the radius.

% Multiply radius image by 2 to get diameter image.

diameterImage = 2 * edtImage .* single(skelImage);

subplot(2, 3, 5);

imshow(diameterImage, [])

title('Diameter Image', 'FontSize', fontSize);

impixelinfo; % Let user mouse around and see values in the status line at the lower right.

% Get the widths. These will be where the image is not zero.

widths = diameterImage(diameterImage > 0);

% Show histogram of widths.

subplot(2, 3, 6);

histogram(widths);

grid on;

xlabel('Width in Pixels', 'FontSize', fontSize);

ylabel('Count', 'FontSize', fontSize);

% Compute the mean width

meanWidth = mean(widths)

% Put a line on the histogram at the mean width

xline(meanWidth, 'LineWidth', 2, 'Color', 'r');

caption = sprintf('Histogram of Widths. Mean Width = %.1f Pixels', meanWidth);

title(caption, 'FontSize', fontSize);

message = sprintf('Mean Width = %.1f Pixels', meanWidth);

msgbox(message);

% Compute the max width

maxWidth = max(widths)

% Put a line on the histogram at the max width

xline(maxWidth, 'LineWidth', 2, 'Color', 'r');

caption = sprintf('Histogram of Widths. Max Width = %.1f Pixels', maxWidth);

title(caption, 'FontSize', fontSize);

message = sprintf('Max Width = %.1f Pixels', maxWidth);

msgbox(message);