Requesting a demo code for calculating second-order statistics (contrast, correlation, energy, homogeneity, and entropy) for medical images.

I want to calculate second-order statistics (contrast, correlation, energy, homogeneity, and entropy) for medical images (CT/MRI images). Do you have a demo code that I can use as an example when writing my own code?

Answer 1

[Walter Roberson]

Split the code up into two parts:

1. read the medical images into memory arrays
2. code that calculates second-order statistics on memory arrays

That is, no calculation of those statistics you list cares even a little whether the arrays are from medical images, or from stock images of happy people and hot air ballons used to sell picture frames, or screen captures from VCR tapes of music videos recorded off of MTV. So go ahead and program the statistics based on receiving 2D arrays (or general arrays), and call those functions passing in the contents of the images you read in.

[Zeynep Fatma]

I have performed semantic segmentation on CT images, classifying them as cancerous and non-cancerous. Then, I calculated the first-order statistics (minimum, maximum, mean, standard deviation, and percentiles 25-50-75) of these regions. Now, I need to calculate the second-order statistics, but I am unsure of how to write the code. Therefore, I requested a demo code from you as an example.

Answer 2

[Image Analyst]

Try graycoprops. I'm also attaching a related GLCM demo and image moments demo. Good luck.

help graycoprops

 GRAYCOPROPS Properties of gray-level co-occurrence matrix.  
    STATS = GRAYCOPROPS(GLCM,PROPERTIES) normalizes the gray-level
    co-occurrence matrix (GLCM) so that the sum of its elements is one. Each
    element in the normalized GLCM, (r,c), is the joint probability occurrence
    of pixel pairs with a defined spatial relationship having gray level
    values r and c in the image. GRAYCOPROPS uses the normalized GLCM to
    calculate PROPERTIES.
 
    GLCM can be an m x n x p array of valid gray-level co-occurrence
    matrices. Each gray-level co-occurrence matrix is normalized so that its
    sum is one.
 
    PROPERTIES can be a comma-separated list of strings or char vectors, a
    cell array containing strings or char vectors, the string "all", char
    vector 'all', or a space separated string or char vector. They can be
    abbreviated, and case does not matter.
 
    Properties include:
   
    'Contrast'      the intensity contrast between a pixel and its neighbor 
                    over the whole image. Range = [0 (size(GLCM,1)-1)^2]. 
                    Contrast is 0 for a constant image.
 
    'Correlation'   statistical measure of how correlated a pixel is to its 
                    neighbor over the whole image. Range = [-1 1]. 
                    Correlation is 1 or -1 for a perfectly positively or
                    negatively correlated image. Correlation is NaN for a 
                    constant image.
 
    'Energy'        summation of squared elements in the GLCM. Range = [0 1].
                    Energy is 1 for a constant image.
   
    'Homogeneity'   closeness of the distribution of elements in the GLCM to
                    the GLCM diagonal. Range = [0 1]. Homogeneity is 1 for
                    a diagonal GLCM.
   
    If PROPERTIES is the string "all" or char vector 'all', then all of the
    above properties are calculated. This is the default behavior. Please
    refer to the Documentation for more information on these properties.
   
    STATS is a structure with fields that are specified by PROPERTIES. Each
    field contains a 1 x p array, where p is the number of gray-level
    co-occurrence matrices in GLCM. For example, if GLCM is an 8 x 8 x 3 array
    and PROPERTIES is 'Energy', then STATS is a structure containing the
    field 'Energy'. This field contains a 1 x 3 array.
 
    Notes
    -----  
    Energy is also known as uniformity, uniformity of energy, and angular second
    moment.
 
    Contrast is also known as variance and inertia.
 
    Class Support
    -------------  
    GLCM can be logical or numeric, and it must contain real, non-negative, finite,
    integers. STATS is a structure.
 
    Examples
    --------
    GLCM = [0 1 2 3;1 1 2 3;1 0 2 0;0 0 0 3];
    stats = graycoprops(GLCM)
 
    I = imread('circuit.tif');
    GLCM2 = graycomatrix(I,'Offset',[2 0;0 2]);
    stats = graycoprops(GLCM2,{'contrast','homogeneity'})
   
    See also GRAYCOMATRIX.

    Documentation for graycoprops
       doc graycoprops