Can I more accurately measure this pixel streak?

For reference, here is the image I am looking at: http://i630.photobucket.com/albums/uu30/Coulton/Image1.jpg

I am trying to find the orientation and center of a streak in an image. The image I am looking at can be found at the link about. For those inquiring, it is a aluminum strip falling in water.

I want to find the orientation and center as mentioned above. I have been able to measure theta fairly accurately. However, when trying to find the center of the streak I obtain a slight difference in the length calculation from the boundingbox cropping image and the overall binary image. Can anyone explain the miscalculation? I am sure it is user error, but I only have a year or so of Matlab experience so I am still learning. Also, is there a way I can measure the center without using for loops and if structures? Let me know if you need more info. The code is below:

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
clear;  % Erase all existing variables.
workspace;  % Make sure the workspace panel is showing.
format longg;
format compact;
fontSize = 20;
Length = input('Enter length of strip (in inches): ');

% Read in a standard MATLAB gray scale demo image.
folder = 'K:\Matlab_Research';
baseFileName = 'image1.jpg';
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
% Check if file exists.
if ~exist(fullFileName, 'file')
  % File doesn't exist -- 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 in the search path folders.', fullFileName);
    uiwait(warndlg(errorMessage));
    return;
  end
end
grayImage = imread(fullFileName);
% Crop away tick marks, colorbar, etc..
grayImage = rgb2gray(imcrop(grayImage, [1 1 725 725]));

% Get the binary image
% Use an algorithm to find the threshold.
binaryImage = grayImage > 100;

% Smooth it out some and clean it up.
binaryImage1 = bwareaopen(binaryImage, 80); 

labeledImage = bwlabel(binaryImage1);
measurements = regionprops(labeledImage, 'all');
% Crop it to the bounding box.
bb = measurements.BoundingBox;

% Crop the image.
binaryImage = imcrop(binaryImage1, bb);

% Skeletonize the image.
skeleton = bwmorph(binaryImage, 'skel', 'inf');

% Find the endpoints of the skeleton.
skeleton = bwmorph(skeleton, 'endpoints');

% Get the coordinates of all endpoints
[rows cols] = find(skeleton);
% Find the two that are farthest apart
maxDistance = -1;
for k1 = 1 : length(rows)
  row1 = rows(k1);
  col1 = cols(k1);
  for k2 = 1 : length(rows)
    row2 = rows(k2);
    col2 = cols(k2);
    distance = sqrt((row1-row2)^2 + (col1-col2)^2);
    if distance > maxDistance
      bestIndex1 = k1;
      bestIndex2 = k2;
      maxDistance = distance;

              % Finding x and y component distances
              y = abs(row2-row1);
              x = abs(col2-col1);
      end
    end
  end

[rows cols] = find(binaryImage1);
% Find the two that are farthest apart from uncropped image
maxDistance1 = -1;
for k11 = 1 : length(rows)
  row11 = rows(k11);
  col11 = cols(k11);
  for k22 = 1 : length(rows)
    row22 = rows(k22);
    col22 = cols(k22);
    distance = sqrt((row11-row22)^2 + (col11-col22)^2);
    if distance > maxDistance1
      maxDistance1 = distance;

              % Store max point locations
              y1 = row11;
              y2 = row22;
              x1 = col11;
              x2 = col22;

              % Find length of strip from uncropped image
              maxDistanceloc = sqrt((y1-y2)^2 + (x1-x2)^2);

              % Determine horizontal location
              if row11 > row22
                  ybar = row11 - abs(row11-row22)/2;
              else
                  ybar = row22 - abs(row11-row22)/2;
              end

              % Deteremine vertical location
              if col11 > col22
                  xbar = col11 - abs(col11-col22)/2;
              else
                  xbar = row22 - abs(row11-row22)/2;
              end
      end
    end
  end

% Angle from horizontal
Theta = atan(y/x)*180/pi;

% Determine pixels per inch
PPI = maxDistance/Length;

% Determine center location from top left of image
xloc = xbar/PPI;
yloc = ybar/PPI;

% Difference in strip length from cropped to uncropped image
SLD = (maxDistance1 - maxDistance)/PPI;

fprintf('\nStrip center location from the top left of the image: ') 
fprintf('y = %2.4f inches and x = %2.4f inches\n\n',yloc,xloc')
fprintf('Theta = %2.2f degrees\n\n',Theta)
fprintf('Difference in strip lengths: %.4f inches\n',SLD)

Answer 1

[Image Analyst]

You can get the centroid location of the binary image before you skeletonize it.

measurements = regionprops(binaryImage, 'Centroid');
centroid = measurements(1).Centriod;