conventional first order statistics such as mean pixel values as well as the second third and fourth moments of those values were insufficient as a result second order statistics are used to evaluate the relationships among the pixel grey level values of the region of interest primarily involving co occurrence and run length matrices none of the standard procedures were found to be adequate for measuring intramuscular fat accordingly three variables were developed for building a multiple regression model these image texture variables include partial autocorrelation correlation in a co occurrence matrix and nonspeckle area these were calculate along pixel vectors that were in the dimension axial to beam transmission appendix i incorporated as part of the disclosure hereof illustrates the preferred program written in fortran for analyzing the image data in computer 16 the partial autocorrelation variable is a time series analysis used to analyze the pixel grey level values along a row in the time domain more particularly the preferred partial autocorrelation is the regression of a pixel value with the pixel value two steps behind it independent of the values of the intervening pixels this is obtained from a formula involving two autocorrelations of a pixel with the pixel at lag 1 and lag 2 the specific formula is where ar2 1 is the partial autocorrelation of the grey level value on the pixel at.