www.pudn.com > image_mva_0.rar > threshold_grayscale_image.m

%                               431-400 Year Long Project  
%                           LA1 - Medical Image Processing 2003 
%  Supervisor     :  Dr Lachlan Andrew 
%  Group Members  :  Alister Fong    78629   a.fong1@ugrad.unimelb.edu.au 
%                    Lee Siew Teng   102519  s.lee1@ugrad.unimelb.edu.au 
%                    Loh Jien Mei    103650  j.loh1@ugrad.unimelb.edu.au 
%  
%  The function without any numbers at the name of its name is the most up-to-date version intended 
%  for use. 
%  Current version in use : Version 6 - threshold_grayscale_image5 
% 
%  File and function name : threshold_grayscale_image 
%  Version                : 6.0 
%  Date of completion     : 2 May 2003    
%  Written by    :   Alister Fong    78629   a.fong1@ugrad.unimelb.edu.au 
% 
%  Inputs        :   original_image  - original grayscale image (0 - black, 255 - white) 
%                    min_threshold   - minimum value that should be recognized as a feature. 
%                    max_threshold   - maximum value that should be recoginized as a feature 
% 
%  Outputs       :   thresholded_image - binary thresholded image  
%                                (Feature = black(0) otherwise white(255) as indicated in Version 5) 
% 
%  Description   :   If the pixel in the original image has a value between the min_threshold  
%                    and max_threshold, then it will be assigned as a white pixel in the binary  
%                    image. Otherwise it will be a black pixel. 
% 
%  To Run >> thresholded_image = threshold_grayscale_image(original_image,min_threshold,max_threshold) 
%            (0 <= min_threshold, max_threshold<= 255) 
 
%                    Version 1 
%                    The speed of this program is too slow 
% 
%                    Version 2 
%                    This is a faster version of threshold_grayscale_image using vectorized methods 
%                    instead of 'for' loops. The previous version took around 56 seconds to execute  
%                    on a 600 x 600 grayscale image, while this version takes about 4 seconds. 
%                    Problem     :   This is still too slow for multiple usage as a 1924 x 2886  
%                                    image takes about 4 minutes to process. 
% 
%                    Version 3 
%                    In this version, the use of vector form of pixel selection to assign the black 
%                    pixel value has improved the speed of this program.  
%                    Problem     :   This is still too slow for multiple usage as a 1924 x 2886  
%                                    image takes about 4 minutes to process. However, the speed  
%                                    bottleneck is now the initialization of the thresholded_image  
%                                    to be all white pixels. 
% 
%                    Version 4 
%                    This is a fastest version of threshold_grayscale_image using vectorized methods 
%                    instead of 'for' loops. The initialization of the thresholded_image has been  
%                    changed to a more vectorized form using a faster 2 step method of initialization. 
%                    Problem     :   This is still too slow for multiple usage as a 1924 x 2886 image  
%                                    takes about 2 minutes to process. 
% 
%                   Version 5 
%                   To speed up the process by about twice the previous speed, we redefine the output 
%                   If there is a feature, it will be represented by white(255) otherwise by black(0) 
%                   Now there is a maximum processing time of about 3 minutes for a 1924 x 2886 image 
% 
%                   Version 6 
%                   Change the resultant image to 0 for black(non-feature) and 1 for white(feature) 
 
function thresholded_image = threshold_grayscale_image(original_image,min_threshold,max_threshold) 
% Initialize the value of black and white pixels 
%black_pixel = 0; 
white_pixel = 1; 
% Ensure that the threshold inputs are acceptable 
if min_threshold > max_threshold 
    error('threshold_grayscale_image : min_threshold is greater then max_threshold'); 
end 
%Generate the resulting image 
%Initialize the image to be all black (non-feature) 
%Method used here is optimized for speed 
thresholded_image = zeros(size(original_image)); 
%Find all pixels which are the feature 
selected = find((min_threshold <= original_image) & (original_image <= max_threshold)); 
%Assign the white pixel value to all coordinates of features  
thresholded_image(selected) = white_pixel;