Shape analysis of surfaces

Abstract

A method and apparatus are provided to map the shape or thickness of a surface. Thickness estimates across a distance are received, and Fourier analysis is performed to generate Fourier amplitude coefficients and phase values for a number of frequency components. Linear discriminant functions are used to characterize the resulting shape as normal or not. Discriminant functions are provided that aid in the diagnosis of glaucoma when the thickness measured is the retinal nerve fiber layer thickness.

1 . An article comprising a machine-accessible media having associated data, wherein the data, when accessed, results in a machine performing: acquiring data representing a nerve fiber layer thickness distribution of a retina; generating a plurality of first frequency component coefficients and phase values by performing a transform on the data representing the nerve fiber layer thickness distribution of the retina; and entering at least one of the plurality of first frequency component phase values into a discriminant function to aid in the diagnosis of glaucoma.  
     
     
         2 . The article of  claim 1  wherein acquiring data representing a nerve fiber layer thickness distribution of a retina comprises acquiring data in a ring around an optic disc.  
     
     
         3 . The article of  claim 1  wherein generating a plurality of first frequency components coefficients and phase values comprises performing a fast Fourier transform (FFT).  
     
     
         4 . The article of  claim 1  wherein determining a plurality of first frequency component coefficients and phase values comprises performing a discrete Fourier transform (DFT).  
     
     
         5 . The article of  claim 1 , wherein the data, when accessed, further results in the machine performing: acquiring data representing a nerve fiber layer thickness distribution of a second retina; generating a plurality of second frequency component coefficients by performing a transform on the data representing the nerve fiber layer thickness distribution of the second retina; and comparing at least one of the plurality of first frequency component coefficients with at least one of the plurality of second frequency component coefficients.  
     
     
         6 . The article of  claim 5 , wherein the data, when accessed, further results in the machine performing: comparing a sum of at least two of the plurality of first frequency component coefficients with a sum of at least two of the plurality of second frequency component coefficients.  
     
     
         7 . The article of  claim 5  wherein comparing comprises comparing a Fourier DC component from the plurality of first frequency component coefficients with a Fourier DC component from the plurality of second frequency component coefficients.  
     
     
         8 . A method comprising: receiving data representing nerve fiber layer thicknesses of a retina; performing a Fourier transform on the data to obtain coefficients representing frequency components and corresponding phase values of the data; and performing a discriminant analysis to determine a discriminant measure from the coefficients to aid in the diagnosis of glaucoma.  
     
     
         9 . The method of  claim 8  wherein the data is divided into hemiretina data prior to performing the Fourier transform.  
     
     
         10 . The method of  claim 8  wherein the data represents the shape of a nerve fiber layer in a ring around an entire retina.  
     
     
         11 . The method of  claim 8  wherein performing a Fourier transform comprises performing a fast Fourier transform (FFT).  
     
     
         12 . The method of  claim 8  wherein performing a Fourier transform comprises performing a discrete Fourier transform (DFT).