Want to find the mean dispersion of the white particles in the image

I have been trying to calculate the mean dispersion of the white particles from an image. The code calculates the mean dispersion but somehow it gives the wrong axis length. I am attaching the code, the results got and the results expected. I have also tried caliberating the image.

clc; % Clear the command window.

close all; % Close all figures (except those of imtool.)

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 = 'bump.tif';

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);

uiwait(warndlg(errorMessage));

return;

end

end

%=======================================================================================

% Read in demo image.

rgbImage = imread(fullFileName);

% Get the dimensions of the image.

[rows, columns, numberOfColorChannels] = size(rgbImage)

% Crop off screenshot stuff

% rgbImage = rgbImage(132:938, 352:1566, :);

% Display image.

subplot(2, 2, 1);

imshow(rgbImage, []);

impixelinfo;

axis on;

caption = sprintf('Original Color 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.

set(gcf, 'Units', 'Normalized', 'OuterPosition', [0 0.05 1 0.95]);

% 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')

drawnow;

% Take one of the color channels, whichever one seems to have more contrast..

grayImage = rgb2gray(rgbImage);

% Display the color segmentation mask image.

subplot(2, 2, 2);

imshow(grayImage, []);

title('Gray Scale Image', 'FontSize', fontSize, 'Interpreter', 'None');

impixelinfo;

axis('on', 'image');

drawnow;

% Show the histogram

subplot(2, 2, 3);

imhist(grayImage);

grid on;

caption = sprintf('Histogram of Gray Scale Image')

title(caption, 'FontSize', fontSize, 'Interpreter', 'None');

xlabel('Gray Level', 'FontSize', fontSize)

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

%=======================================================================================

% Threshold the image to get the disk.

lowThreshold = 205;

highThreshold = 255;

% Use interactive File Exchange utility by Image Analyst to to the interactive thresholding.

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

% [lowThreshold, highThreshold] = threshold(114, 255, grayImage);

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

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

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

% Now do clean up by hole filling, and getting rid of small blobs.

%mask = bwareafilt(mask, 1); % Take largest blob only.

% Fill the mask from the top most pixel downwards in the column.

% for col = 1 : columns

% topRow = find(mask(:, col), 1, 'first');

% if ~isempty(topRow)

% mask(topRow:end, col) = true;

% end

% end

% Display the color segmentation mask image.

subplot(2, 2, 4);

imshow(mask, []);

title('Final Mask', 'FontSize', fontSize, 'Interpreter', 'None');

impixelinfo;

axis('on', 'image');

drawnow;

% [y1,x1]=find(mask==1);

% miny1=min(y1);

% maxy1=max(y1);

% scanint=1;

% save_mat=[];

% currentvaly=min(y1)

% for i1=1:(maxy1-miny1+1)

% yloc=find(y1==currentvaly)

% ynow=currentvaly;

% xnow=zeros(length(yloc),1);

% for j1=1:length(yloc)

% xnow(j1)=x1(yloc(j1));

% end

% summ=0;

% xavg=mean(xnow);

% for j1=1:length(yloc)

% summ=(xavg-xnow(j1))^2;

% end

% Dispersion1=sqrt((1/length(yloc))*summ);

% savedata=[ynow,Dispersion1];

% save_mat=[save_mat; savedata];

%

%

%

% currentvaly=currentvaly+1;

% end

[y1,x1]=find(mask==1);

minx1=min(x1);

maxx1=max(x1);

scanint=1;

save_mat=[];

currentvalx=min(x1)

for i1=1:(maxx1-minx1+1)

xloc=find(x1==currentvalx)

xnow=currentvalx;

ynow=zeros(length(xloc),1);

for j1=1:length(xloc)

ynow(j1)=y1(xloc(j1));

end

summ=0;

yavg=mean(ynow);

for j1=1:length(xloc)

summ=(yavg-ynow(j1))^2;

end

Dispersion1=sqrt((1/length(xloc))*summ);

savedata=[xnow,Dispersion1];

save_mat=[save_mat; savedata];

currentvalx=currentvalx+1;

end

figure;

plot(save_mat(:,1),save_mat(:,2),'k.')

uiwait(helpdlg('Done!'));

figure;

plot(y1,x1,'k.')