Saturday, December 1, 2012


A Gentle Introduction to Matlab for Image Processing

Downloads and Preparation

To work through the tutorial, you will need to go to
Get cnl_mfiles.zip and Arrays.zip.  If you would like to make your own *.img files into matlab arrays, then you will need spm installed. A practice set of spm *.img files is also available for download, cnl_leftles_data.zip.  The m-files should work under unix, but haven't been tested.
You should have Matlab installed and know how to start it.  This tutorial is designed for Matlab 6.0, which has many new friendly point and click (Graphical User Interface) features.  If you are using an older version of Matlab, then references to the gui won’t make sense.
To use “loadimg.m”  (in the cnl_mfiles.zip), you will need to have spm99 installed.  If you don’t have spm, you can use the imgarray and anatarray files (from the Arrays.zip download), to do everything in the tutorial. These images help you keep track of left-right flips because they are from a subject with a large left frontal lesion. Original 2D and 3D structural img files are at: http://merlin.psych.arizona.edu/cgi-bin/wrap/dpat/Public/Imaging/SPM/TestImages/.  The entire functional data set in *.img format is in the cnl_leftles_data.zip download in the matlab area on merlin (see above URL).

Getting Started

Matlab is a programming language and interface that is especially good at dealing with matrices of numbers. Read the CNL matlab web page  (http://cnl.web.arizona.edu/matlab.htm) before continuing with this tutorial. When you start Matlab, you will see a screen divided into several windows: 
The large window on the right is the Command window.  Here you can display variables, type commands etc.
On the lower left, are two tabbed windows, the Current Directory window which allows you to see and manipulate files in your current directory, navigate to other directories etc., the Command History window which keeps track of all the commands you’ve typed as well as copying those commands to the current command line or rerunning (evaluating) the lines.
On the upper left is the Workspace window.  This window displays all current variables and allows you to perform various operations on them by right clicking and selecting from the popup window.
On the menu bar, the File menu allows you to create new files, open files, import data, and set the path (see below).  The Help menu gives you access to extensive online help files.
Conventions: I will show you what to type at the matlab prompt “>>” in bold black (don’t type the prompt).  Comments will adhere to Matlab conventions:  preceded by a “%” and in green.  Matlab will not try to interpret anything on the line after a percent sign.

 

 

Setting the Path
Unzip cnl_mfiles.zip in a directory where you want to work.  Matlab finds m-files by looking in all the directories in its “path”. To tell Matlab where to look for these new m-files, click file->set path.   You should see the “Set path” window. Click “Add Folder” or “Add with subfolders” (depending on how many levels of directories you want Matlab to search for m-files). Browse to the directory you want to add. Select it and click “Ok” .
Now try each of the following commands:
>> testabc        % Should create 3 variables, testa, testb and testc (if the path was set correctly)
>> testa            % Look at a variable by typing its name
>> who             % Tells you what your variables are (same as looking in the workspace window)
>> whos           % Tells you more about your variables
>> what            % Enumerate matlab files in current directory (make sure you are a directory where there are some matlab files).
>> ls                % Lists files in current directory (like looking at the Current Directory window)
>> save testy % Saves current variables in a *.mat file called testy in the current directory
>> load testy % Looks for testy.mat and loads its variables into the workspace.
>> clear testb % Removes the specified variable from the workspace
>> clear               % Removes all loaded variables from the workspace
>> delete testy.mat  %removes the saved testy.mat from the computer

M-files

Use File->Open (Browse) to find and display m-files. As you progress, m-files should make more sense.  Within m-files, select and right-click for a menu:  For keyword help choose “help selection”. To run parts of the program and see what they do, choose “evaluate selection”.  When you get to "Control Structures", use File->New->M-file to create your own blank m-file.

Vectors and Arrays

Matlab works with rectangles of numbers: vectors, and matrices (arrays).  A vector is a 1-D matrix or array, a matrix or array can have any number of dimensions (Figure 1 illustrates 1-D, 2-D and 3-D arrays). Arrays can represent images:  A 2-D matrix is like an image slice. A 3-D matrix is like a set of slices comprising a brain.  A 4-D matrix is like an fMRI dataset representing a series of  3-D brain images through time.
 

 

 

 

 

 

 


   Figure 1

 

Creating Vectors and Arrays

You can create the row vector “a”, of values 1 2 3 4, in several ways:
>> a = [1 2 3 4]
>> a = [1:4]
>> a = (1:4)
>> a = [1:1:4]
>> a = (1:1:4)
Typically [ ] are used for concatenation, e.g., [1 2 3 4].
The other statements are not really concatenation statements so either () or [ ] seem to work. The colon operator is used in several ways: In a=[1:4] and a=(1:4) it  means “1 thru 4”, or “start with ‘1’and count up to ‘4’”.
In a=[1:1:4] and  a=(1:1:4) a value is inserted in the middle that refers to the increment.  These say “Start with ‘1’, count up by ‘1’ (the default), end at 4.  Think of this latter as the basic way to write the statement, and think of  a=[1:4] and a=(1:4) are a shorthand that works because the default increment is ‘1’.
Column vectors can be created from scratch:
>> a2 = [1;2;3;4]
Or you can transpose a row vector to create a column vector:
>> a2 = a’
Either of the above produces a2:
1
2
3
4
The 2-D matrix in Fig. 1, can be created by putting a hard return between rows:
>> b = [1   4   7  10
2   5   8  11
3   6   9  12]
or by putting semicolons between rows:
>> b = [1 4 7 10; 2 5 8 11; 3 6 9 12]
A 3-D matrix is more difficult:
>> c(2,3,2) = 0
creates a 3-D array with 2 rows, 2 columns and two pages, but it is filled with zeros.  How might you create the 3-D array in Figure 1?:
>> c = [6 3 2 5 1 4 7 6 8 4 9 3]        
>> c = reshape(c, 2, 3, 2)

Describing Arrays

>> size(a)         % Size as an ordered list: #rows, #columns, #pages etc.
>> ndims(a) % Number of dimensions
>> numel(a) % The number of elements in the array.
>> length(a) % Equivalent to max(size(a)), returns the largest dimension of “a”.

Counting and Indexing Elements

 For a 2-D matrix, start counting elements at ‘1’ in the upper left corner and count down to the bottom of the first column, then start counting again at the top of the next column (see the 2-D matrix in Figure 1). For the 3-D matrix, count the same way, starting on page 1 (a “page’ in Matlab is like a slice in an image), then going to page 2, etc. 
Each array element has a simple index based on the count (e.g.,  1, 2, 3 etc.) AND an index based on array shape (like a Cartesian Coordinate); e.g., the simple index “1” corresponds to the 2D index “1,1” or the 3D index “1,1,1”.  “2” corresponds to the 2-D “2,1” etc.
Finally, each element in an array has a value assigned to it.  For our grayscale Mri and fMRI images, this value represents intensity: black is zero, white is the largest value (256 for an 8 bit image, 65,536 for a 16 bit image).
To find the value of an element/voxel, simply enter a valid index for it. Consider the 3-D array, Figure 1:
>> size(c)         % 2 rows, 2 columns and 2 pages (like 2 slices from an image) 
>> c(1)              % Produces the answer (ans) “6”. “6” is the value
>> c(1,1,1)        % Same as above
>> c(2)              % Has the value “3”
>> c(2,1,1)        % Same as above
>> c(7)              % Has the value “7”
>> c(1,1,2)        % Same as above

Extracting Subarrays (pg 39 of Mastering Matlab)

You can also extract a subset of elements from an array:
>> a(1:end)   % Gets the whole matrix “a”, beginning with the first element
>> a(3:end)   % Gets elements 3 through the last element of “a”
>> a(3:4)       % Gets the third and fourth elements of “a”
>> a(4:-1:1)   % Start with 4th element of “a”: goes backwards in increments of 1:
% End with the first element of “a”.
>> a([3 1])      % Selects the third and first element from “a”
>> b(:,2)         % Gets elements in all rows [“:” means “all”] for column 2 of “b”
>> b(2,:)         % Gets elements in all columns for row 2 of “b”
The above examples only show you the output values. To extract the subarray  into a variable, you need a variable name (e.g., “d”) and the assignment operator (= is the assignment operator, not to be confused with  ==,  the equal sign), e.g.:
>> d = a(4:-1:1)  
produces “d”: d= 4 3 2 1
Here’s another one, a bit more complex:
>>d2 = c(1:2,2:3) % Extract elements that are in both the first 2 rows of c and the second and
       % third columns of c to create d:
2  5
1  4

Array Concatenation (pg 42 of “Mastering Matlab”)

>> e = [a d]                               % This works if  “a” and “d” have the same number of rows.
>> e = c(:,[2 2 2 2])                    % Create e using all rows “:” of column 2 concatenated 4 times.
The next two commands accomplish exactly the same thing:
>> e = c(:,2+zeros(1,4))
>> e = repmat(c(:,2),1,4)            % Replicate c (all rows, column 2; create columns 1 thru 4 of “e”.
e =2 2 2 2
     5 5 5 5
% Create “f” by concatenating “d2” with all rows % Of  “e” in the first thru second columns.
 
>> f = [d2 e(:,[1:2])]
2 5 2 5
1 4 2 5
>> g = cat(3,e,f)                        % Concatenate “e“ and “f“ along the 3rd dimension to create “g“
Array Manipulation (pg 57 of “Mastering Matlab”)
Clear the workspace and create “a” as a 2-D matrix:
1 2 3
4 5 6
7 8 9
Think of several ways to create a fresh copy of “a” for each manipulation.
>> a(:,2) = 9  % For all rows in a, set elements in column 2 = 9:
1 9 3
4 9 6
7 9 9
>> a(2,2) = 9  % In row 2, column 2 (their intersection), set elements to “9”:
1 2 3
4 9 6
7 8 9


% Start with row 3 of “a”
% Go backwards by 1’s (-1)
% End with row 1
% Use columns 1:3
 
 
>> b = a(3:-1:1,1:3)
7 8 9
4 5 6
1 2 3


% Start with row 1 of “a”
% Increment by 1’s
% End with row 3
% Use columns 1:3
 
 
>> b = a(1: 1:3,1:3)
1 2 3
4 5 6
7 8 9


% Start with row 3 of “a”
% Go backwards by 1’s (-1)
% End with row 3
% Use columns 1:3
 
 
>> b = a(3: -1:3,1:3)
7 8 9
% Start with row 3 of “a”
% Go backwards by 1’s (-1)
% End with row 1
% Use columns 2:3
 
>> b = a(3: -1:1,2:3)
8 9
5 6
2 3

Merging Arrays

a=        1 2 3                 b=4 5 4
4 5 6                     4 5 4
7 8 9                     5 4 5
>> b(3:4,:) = a(1:2,:) creates
% For rows 3:4 of “b”(whether or not they exist), all
% columns, substitute all columns of rows 1:2 from “a”.
 
b=        4 5 4
4 5 4
1 2 3
% Put the contents of columns 2 thru 3 from “b”
% into a row vector % called “g”.
 
4 5 6
>> g(1:8) = b(:,2:3) 
creates:
5 5 2 5 4 4 3 6
% The first command creates “h”, a column
% of 6 ones.  “h” can then be filled with
% values from some other array, in this case
% “a”, without changing the shape of “h”.
 
>> h = ones(6,1)
>> h(:) = a(:,2:3)
2
5
8
3
6
9
Array Reshaping (pg 63, Mastering Matlab)
For reference, “a” is:            1 2 3
4 5 6
7 8 9
>> b = a(:)                                % Reshape “a” into a column “b”
>> b = reshape(a,1,9)    % Reshape “a” into a 1x9 vector “b” (a row vector)
>> b = a’                                  % Transpose a to create b (transpose is not equivalent to reshape)
1 4 7
2 5 8
3 6 9
>> b(2,:) = [ ]                % Remove row 2 from “b”
1 4 7
3 6 9
>> a(2,:) = b(2,:)                        % Replace row 2 of “a” with row 2 of “b” (with most recent “b”):
1 2 3
3 6 9
7 8 9
>> a(2,:) = 6                  % Replace all elements of row 2 in “a” with “6’s”.
>> a(2,:) = [ 6 6 6]         % Same as above, but without relying on scalar expansion of “6”
1 2 3
6 6 6
7 8 9

Substituting Values in Arrays

a=        0 0 0
            0 0 0
            0 0 0
% Put the value “1” in row 1, the
% first and third elements.
 
>> a(1,[1,3]) = 1
1 0 1
0 0 0
0 0 0
% Run on the original “a”, this puts the
% value “2” into the first and third
% elements of column 1.
 
>> a([1 3], 1) = 2
2 0 0
0 0 0
2 0 0
Standard Arrays (pg 52, Mastering Matlab)
Matlab can create arrays of all zeros, all ones, or all random numbers between 0 and 1.
>> a = zeros(1,4)          % Creates array of zeros, “a”, with one row and 4 columns.
>> b = ones(1,4,3)        % Creates array of ones, “b”, with one row, 4 columns, and 3 pages
>> c = rand(2,3,4,5)      % Creates random number array “c”, 2 rows, 3 columns, 4 pages
 % and 5 4th dimensional components (Values from 0 to 1).

More Array Manipulations

>> d = permute(c,[1,2,4,3])  % Permute the ordering of dimensions in c (a 4-D matrix), by switching the third and 4th dimension. This particular permutation would change an array of concatenated *.img files  into a “BRIK” style array, (i.e., img files are sorted first by physical slice (page), then by time; BRIK files are sorted by time first and physical slice afterwards.
>> d = find(c==0)  % Create a vector “d” with indices to every 0 valued element in imgarray.
>> d = squeeze(c)  %Create “d” which contains the same values as “c” but gets rid of any extraneous singleton dimensions (Since “c” doesn’t have any dimensions of “1”, this won’t do anything.
________________________________________________________________

Sample Mathematical Operations on Arrays

>> c^2              % c-squared (c can be a matrix).
>> d = (c.*a)     %This is dot multiplication, it does element by element multiplication of 2 equal
% sized arrays; in this case,  “c” and “a”. Use dot multiplication to apply a binary
% (1,0) mask to an image.
________________________________________________________________

Cell Arrays

A cell array is a useful structure to know about if you want to work in SPM.  A cell array can hold different sized vectors in each cell.  In SPM, you can use the cell array to hold a vector of stimulus onsets for each of several conditions (e.g., the vector for the first condition is in cell 1.  The vector for the second condition is in cell 2 etc.)
To create a cell array:
>>a{1,1} = [1 2 4 6 8]
>>a{1,2} = [ 5 77 89]
>>a{1,3} = [3 4 5 6 2 1 7 8 9 334]
You now have a cell array, a, that contains 3 row vectors (this is perfect for SPM stimulus onset times) in 3 cells.
To view a description of the cell array:
>>a
To see the contents of cell 1:
>>a{1,1}
________________________________________________________________

Some Basic Control Structures

For Loop: The for loop is generally a non-preferred structure in Matlab because usually array operations can substitute.  Note that by default, a for loop outputs scalar values and overwrites them each time (unless you concatenate them into an array).  Nested for loops are complicatedMatrix operations produce other matrices by default:
for x = a                       % Where “a” is an array
x + 5                             % Add 5 to each element. Note that x is overwritten by each calculation
end  
Alternative:  a + 5      % Add 5 to each element in “a”.  This is the “matrix” solution
___________________________________

If-else-end

If statements come in levels of complexity depending on how many conditions (1,2 or more):
1)    if-end,
2)    if-else-end,
3)    if-elseif-else-end
1) A simple if statement:     if apples > 5;
cost = 20;
end;
2) if-else statement, 1 very simple and one incorporating an “and” statement:
if apples > 5;
   cost = 20;
else;
    cost = 25;
end;
if (a == 5) & (c == 2); %note equal sign
    b = 2; % assignment here
else;
    b = 3;
end;
3) A more complex if-elseif-else statement         if apples > 15;
                                                                                    cost = 20;
elseif apples > 12;
                                                                                    cost = 22;
elseif apples > 10;
                                                                                    cost = 23;
else;
                                                                                     cost = 25;
end; 

Putting it All Together

Here’s a program that takes an input array, calculates a global mean for that array, and then creates a second array (binmask) that puts a zero in every element less than the threshold and a one in every other element. Our real solution in binmask.m is a little more complex, since we want to fill each time vector with 0’s if it averages less than the threshold and fill all other time vectors with 1’s.  Nevertheless, this is a good starting point.  Look at picker.m (for loop that gets voxels like this one), rowpicker.m (for loop that gets rows), and historypicker.m (for loop that gets vectors along the 4th dimension).
Here you see how to request user input from the command line, use functions like size, mean2 and reshape, employ "for" and "if-else" control structures, and use a number of matrix manipulations (matrix multiplication, creating an empty matrix, specifying subarrays, and concatenating and reshaping arrays)
in_array = input('Enter the name of loaded array > ');       %Request user input
[r,c,nas,ntr] = size(in_array);                                           %dim1=rows, dim2=columns, dim3=# of anatomical %slices, dim4=# of temporal repetitions
GlobalMean = mean2(in_array);                          %Calculate mean for 4-D array
Thresh = .75*GlobalMean;                                              %Create a threshold at 75% of the mean
binmask = [];                                                                 %Create empty variable binmask
for vr = 1:r;                                                                   %define vr as all rows from 1 to r (for all rows)
    for vc = 1:c;                                                              %for all columns
        for vnas = 1:nas;                                                   %for all slices
            for vntr = 1:ntr;                                                  %for all timepoints
                  a = in_array(vr,vc,vnas,vntr);                                    %set a to each element in the array
                     if a > Thresh;                                            %if a is > Thresh
binmask = [ binmask 1 ];           % set the mask element to 1 and concatenate it to the %existing binmask
                        else                                                      %otherwise
 binmask = [ binmask 0 ];          % mask element=0 and is concatenated onto binmask
                end;
            end;
        end;
     end;
 end;
 binmask = reshape(binmask,r,c,nas,ntr);                        %reshape output to be the same shape
                                                                                    % as in_array

Viewing your Matrix

Providing you have spm and the program loadimg.m, you can load a 4D array of data into the main workspace and play with it.  You will need a 4D data set saved in *.img format. In these examples, we use imgarray (from Array.zip). imgarray is a 4-D data set from a patient with a large left frontal lesion.  The array is 64x64x17x80. To view any “slice” of the data in a 4D matrix, use surface or surf:
>>surface(imgarray(:,:,7,1) % View the slice that includes all rows and all columns of the seventh physical slice at time 1.
>>surf(imgarray(:,:,7,1) % View the slice that includes all rows and all columns of the seventh physical slice at time 1. The difference is that it comes up at a different angle by default and that surf is a “high level” function that allows the user more control over display properties.
Note that for both “surface” and “surf” plots, you can rotate the plot and see it displayed as a topo map.  You can also annotate it, display or change the color bar, etc. If you imported your image from Afni with AFNItoANALYZE, and your original BRIK was in radiological orientation (as it generally would be in Afni), then viewing the array with surf or surface will show you an image in radiological orientation.  However, that same image, viewed with imshow (see below) will be switched into neurological orientation. In addition, for both imshow and surface, the z axis (dim 3) will be flipped relative to the Afni slices (e.g.,  in a 12 slice image, slice 0 in Afni will be slice 12 in Matlab, Afni slice 1 will be Matlab slice 11 etc.).
>>plot(imgarray(:,32,4,2)) % Make an intensity line plot of all columns at the point where they intersect row 32 of the fourth physical slice at the second timepoint (in effect, plot row 32 of slice 4, timepoint 2).
>>plot(imgarray(32,:,4,2) % Make an intensity line plot of all rows at the point where they intersect column 32 of the fourth physical slice at the second timepoint (in effect, plot column 32 of slice 4, timepoint 2).
>>a=(imgarray(:,:,:,1) % Extracts all values (a 3D brain volume) at timepoint 1.
>>slice(a,32,32,10) %Creates figure of 3 planes from "a" intersecting at x(row)=32, y(column)=32 and z=10.
Right click any variable in the Matlab workspace to find 2D and 3D graphing options.  Keep in mind that once you have displayed a figure window,  it may simply minimize and you won’t see the results of your graphing unless you go click the figure window in the start bar or request a new figure window:
>>figure

Basic Statistics on Arrays

>>mean2(imgarray) %returns the mean value for an array of any number of dimensions.  Requires the image processing toolbox.
At least two other statistics for n-dimensional arrays, corr2 and std2 that come with the image processing toolbox
>>mean(mean(mean(mean(randarray)))) % same result as above for a 4-D array (4 levels of nesting for the 4D array), but doesn’t require the image processing toolbox.
You can use the same technique for the standard functions std (the standard deviation of the array), min (the minimum value in the array), and max (the maximum value in the array). Normally, these simple statistics work on 1-D vectors, but if you nest them for the number of dimensions, as in the above example, they can be made to work on any sized array. Check out zscore.m in cnl_mfiles.

Fun with Guide

You can create graphical user interfaces with guide. cnlimg is one such interface.
>>cnlimg                     % Brings up the figure window with active buttons.
>>guide cnlimg            % Brings up cnlimg in edit mode.
In edit mode, you can right click any button and choose “Inspect properties”.  Simply set the Callback property to be the name of the m-file that the button should run when pressed.
You may wish to set “tooltip string” to be the explanatory help that comes up when someone mouses over the button.
>>guide   % Brings up the gui development environment so you can make your own interface.

Fun with the Image Processing Toolbox

If you have the optional image processing toolbox, then you can do some other nice  things.
>>ver %This will tell you the version of matlab you have and which toolboxes you have
The image processing toolbox will not automatically treat the results of running loadimg (which requires spm) as images, because it expects images to contain certain structures.  For example, the image type we are primarily interested in, intensity images, must have values between 0 and 1. imgarray.mat in Arrays.zip does not contain the correct values.  You can use mat2gray to convert it:
>>imgarray2 = mat2gray(imgarray);
A slice out of imgarray2 can now be directly displayed as a proper intensity image:
>>imshow (imgarray2(:,:,7,3)); %A figure window should come up.  If it does not, type:
>>figure,  imshow (imgarray2(:,:,7,3));
You can get imshow to interpret your array values and display a slice without converting with mat2gray:
>>figure,  imshow (imgarray2(:,:,7,3), []); % add an empty matrix [] to the statement.
But if you are interested in playing with other image processing functions, you will run into trouble unless you do the mat2gray conversion. anatarray is a 256x256x17 mri structural image, already converted with mat2gray.  Note that the simple mat2gray conversion  produced an image that was way too dark.  By default, the min value and max value in the array are used as the limits (In this case 0 and 1454).  However, a histogram of one of the slices revealed that most values in the image were less than 300 and only a few ranged between 300 and 750 on a typical slice.  Explicitly setting a tighter range, based on the histogram, brightened up the image considerably:
>>anatarray2 = mat2gray(anatarray, [0, 500]);
Other ranges are possible. 1000 looked better than 1454, but I liked 500 best.  You should be very reluctant to crop the upper part of the range for a functional image, since that range provides vital information.  However, cropping the range for a structural should be less crucial, especially if it improves your ability to resolve features by eye.
Now that you have a figure window up, displaying a slice, type:
>>roipoly  % This allows you to draw a shape on a displayed image which it automatically makes into a binary mask, so if you draw around the brain, then an image will be produced in which everything outside the brain is black (0) and everything inside is white (1).
>>impixel % This allows you to select points (a little star will appear at each point) on a displayed image.  When you hit enter, the intensity values are returned for each star (they are presented in RGB format, for an intensity image, this means you'll get a row of 3 equivalent values R=G=B for each star). Similar to picker.m.
>>improfile % This allows you to draw a line on a displayed image, or series of line segments on the image and get back an intensity profile.  A complex set of line segments results in a 3-D histogram.  Similar to rowpicker.m and historypicker.m.
>>imcontour (imgarray(:,:,7,3)) % This draws an edge contour of the slice, even if the slice is not from a mat2gray converted array.
>>imhist (imgarray(:,:,7,3)) % Creates an image histogram, seems to be finicky about the range of values (It may require a true intensity image converted with mat2gray).  "hist" will work on any slice of an array, whether or not it is in the "image" range.
Montage creates a montage of multiple images. Mov creates a movie. Both montage and mov expect the multiple images to be arranged on the 4th dimension.  montage requires that the image be converted with mat2gray first.  mov require that the image be converted from an intensity image to an indexed image.  The following steps should work to create a montage and movie respectively:
>>anatmontage = reshape(anatarray, [256,256,1,17]); % Create anatmontage by reshaping anatarray so that it has an extra singleton dimension and the slices are arranged on the 4th dimension.  Remember anatarray has already been converted to a true intensity image with mat2gray.
>>figure, montage(anatmontage) %This should bring up a figure window and display the montage.
 
To create a movie:
>>[ind_anat, gray] = gray2ind(anatmontage, 256); %This converts the image from intensity to indexed. Indexed images havetwo parts, an image and a colormap.  In this example, "ind_anat" is the indexed image and "gray" is the colormap.
>>anat_mov=immovie(ind_anat, gray); % This creates the movie (and should play it once)
>>movie(anat_mov) % This plays the movie

Summary

This should get you started.  Look on-line for m-files, and look at the m-files you have.  The ones you’ve downloaded with this tutorial are pretty straightforward (load imgarray or anatarray and then type the name of the m-file you want to run).  Look at simple ones like mkmatrixrand.m, and zscore.m.  See corall9.m to see a switch-case statement. Let me (dpat@u.arizona.edu) know when you have suggestions or find errors.  Thanks, Dianne.

Functions Covered

(*=Image Processing Toolbox)

cat
clear
corr2 *
delete
figure
find
for
gray2ind *
guide
hist
if-else
imcontour *
imhist *
immovie *
impixel *
improfile *
imshow *
length
load
ls
mat2gray *
max
mean
mean2 *
min
montage *
movie *
ndims
numel
ones
permute
plot
rand
repmat
reshape
roipoly *
save
size
squeeze
std
std2 *
surf
surface
ver
what
who
whos
zeros

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