U.S. patent application number 11/337329 was filed with the patent office on 2006-06-22 for shape analysis of surfaces.
This patent application is currently assigned to University of Louisville Research Foundation, Inc.. Invention is credited to Edward A. Essock, Michael J. Sinai.
Application Number | 20060135867 11/337329 |
Document ID | / |
Family ID | 26831212 |
Filed Date | 2006-06-22 |
United States Patent
Application |
20060135867 |
Kind Code |
A1 |
Essock; Edward A. ; et
al. |
June 22, 2006 |
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.
Inventors: |
Essock; Edward A.;
(Anchorage, KY) ; Sinai; Michael J.; (Louisville,
KY) |
Correspondence
Address: |
VIKSNINS HARRIS & PADYS PLLP
P.O. BOX 111098
ST. PAUL
MN
55111-1098
US
|
Assignee: |
University of Louisville Research
Foundation, Inc.
|
Family ID: |
26831212 |
Appl. No.: |
11/337329 |
Filed: |
January 23, 2006 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10133268 |
Apr 26, 2002 |
|
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11337329 |
Jan 23, 2006 |
|
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60286654 |
Apr 26, 2001 |
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Current U.S.
Class: |
600/410 |
Current CPC
Class: |
G06T 2207/30041
20130101; A61B 3/10 20130101; G06T 2207/20056 20130101; A61B 3/102
20130101; G06T 7/0012 20130101; G06T 2207/10101 20130101; G06K
9/522 20130101 |
Class at
Publication: |
600/410 |
International
Class: |
A61B 5/05 20060101
A61B005/05 |
Claims
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).
Description
RELATED APPLICATION
[0001] This application claims priority to U.S. Provisional Patent
Application No. 60/286,654, filed Apr. 26, 2001, the entirety of
which is incorporated herein by reference.
BACKGROUND
[0002] Many conditions and diseases are associated with
abnormalities of a surface. The existence and severity of the
condition can be diagnosed by plotting data measurements of
thickness of the surface layer, that is, height from a chosen base
layer and comparing the results to a normal surface, that is, one
that is not diseased. These conditions may include coronary artery
disease, abnormal angiogenesis as in diabetic retinopathy and blood
vessel thrombi. The eye disease glaucoma, a leading cause of
blindness, is characterized by alterations in the retinal nerve
fiber layer.
[0003] It is well known that the necessary point measurements can
be obtained by scanning laser polarimetry (SLP), ocular coherence
tomography (OCT), ultrasound, photogrammetry or the like. However,
the reliability and power of such methods are dependant on the
mathematical analysis of the data. Some previous methods selected a
normal value for the thickness of the surface in question and
determined individual variations from normal values.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1 shows a flowchart that describes various method
embodiments of the invention;
[0005] FIG. 2 is a diagram of an apparatus in accordance with
various embodiments of the invention;
[0006] FIG. 3 shows a first plot of a "double hump" pattern of
nerve fiber layer thickness values obtained from a circular ring
around a retina;
[0007] FIG. 4 shows a plot of the result of a Fourier analysis of
the superior hemiretina NFL thickness data shown to the left of the
dashed vertical line in FIG. 3;
[0008] FIG. 5 shows a second plot of a "double hump" pattern of
nerve fiber layer thickness values obtained from a circular ring
around a retina;
[0009] FIG. 6 shows a plot of the result of a Fourier analysis of
the entire double-hump pattern shown in FIG. 5;
[0010] FIG. 7 shows a distribution of Fourier components and GDx
numbers for normal and glaucomatous eyes;
[0011] FIGS. 8, 9, and 10 show Receiver Operator Characteristic
(ROC) curves for the measures shown in FIG. 7;
[0012] FIG. 11 shows Fourier components for both normal and
glaucoma patient groups;
[0013] FIGS. 12, 13, and 14 show NFL thickness data for three
sample populations;
[0014] FIG. 15 shows NFL thickness data for the normal eyes shown
in FIGS. 12, 13, and 14;
[0015] FIG. 16 shows NFL thickness data for the glaucomatous eyes
shown in FIGS. 12, 13, and 14;
[0016] FIG. 17 shows NFL thickness data taken by a scanning laser
polarimeter (SLP);
[0017] FIG. 18 shows NFL thickness data taken by an ocular
coherence tomograph (OCT);
[0018] FIG. 19 shows data from FIGS. 17 and 18 normalized and
plotted together;
[0019] FIGS. 20, 21, and 22 show ROC plots for GDx data; and
[0020] FIGS. 23, 24, and 25 show ROC plots for OCT data.
DESCRIPTION OF EMBODIMENTS
[0021] In the following detailed description, reference is made to
the accompanying drawings that show, by way of illustration,
specific embodiments in which the invention may be practiced. It is
to be understood that various embodiments of the invention,
although different, are not necessarily mutually exclusive. For
example, a particular feature, structure, or characteristic
described herein in connection with one embodiment may be
implemented within other embodiments without departing from the
spirit and scope of the invention. In addition, it is to be
understood that the location or arrangement of individual elements
within each disclosed embodiment may be modified without departing
from the spirit and scope of the invention. The following detailed
description is, therefore, not to be taken in a limiting sense, and
the scope of the present invention is defined only by the appended
claims, appropriately interpreted along with the full range of
equivalents to which the claims are entitled. In the drawings, like
numerals refer to the same or similar functionality throughout the
several views.
[0022] In various embodiments of the present invention, the shape
of a distribution of measurements of a surface area is determined
by scanning the surface with a measuring system and applying
mathematical analyses to the resulting data points in order to map
the surface. The measuring system may be a scanning laser
polarimeter (SLP), an ocular coherence tomograph (OCT), or any
other device capable of measuring the shape of a surface area. Such
devices may be of the appropriate size for the surface to be
analyzed and may be fitted with access mechanisms such as fiber
optic cannulae for such surfaces that are not readily
accessible.
[0023] The shape of the distribution of measurements form a graph
of the particular measured value plotted across distance. The
mathematical analysis is directed at characterizing the shape of
the distribution of measurements across the surface. Various
methods analyze shape in part by obtaining amplitude and phase
values using a Fourier transform. In some embodiments of the
invention, these factors are then combined in a linear discriminant
function or other methods, and the resultant value(s) is then used
to distinguish one group of surfaces from another, that is,
diseased from normal. The use of embodiments of the invention in
the diagnosis of glaucoma is described in detail.
[0024] FIG. 1 shows a flowchart that describes various method
embodiments of the invention. Some embodiments of the invention
generate Fourier-based discriminant functions for the diagnosis of
glaucoma. Other embodiments of the invention use the Fourier-based
discriminant functions to aid in the diagnosis of glaucoma. FIG. 1
represents embodiments that generate Fourier-based discriminant
functions, as well as embodiments that use Fourier-based
discriminant functions. Each of the actions in the flowchart are
described briefly here, and are described in more detail in later
portions of this description.
[0025] In block 102, nerve fiber layer (NFL) thickness data are
imported from a given device and format. As previously described,
the device may be a scanning laser polarimeter (SLP), an ocular
coherence tomograph (OCT), or any other device capable of measuring
the shape of a surface area or the thickness of the NFL. The data
may also be of any suitable format. Examples include, but are not
limited to, a complete mapping of the NFL thickness in rectangular
or polar coordinates, or a partial mapping. Partial mappings may
include hemiretinal NFL measurements, NFL measurements of quadrants
of the eye, or any other subset of a complete NFL thickness
mapping. Some embodiments described in more detail below include
NFL thickness measurements within an annular region (or "ring") of
the NFL at a given radius from the center of the optical disc.
Other suitable formats include data that is partially processed.
For example, in some embodiments, imported data may be a set of NFL
thickness averages, where each average represents the average
thickness over a defined region of the eye.
[0026] In block 104, a Fourier transform is applied to the data to
yield Fourier coefficients and phase values. In some embodiments,
the Fourier transform is applied using a discrete Fourier transform
(DFT), and in other embodiments, the Fourier transform is applied
using a fast Fourier transform (FFT). Any number of Fourier
coefficients and phase values can be generated without departing
from the scope of the invention. The Fourier transform can be
applied to NFL thickness data representing the entire eye, or a
portion thereof. For example, in some embodiments of the invention,
the Fourier transform is applied to data representing each
hemiretina separately, and in other embodiments, the Fourier
transform is applied to data representing a ring about the whole
retina.
[0027] In block 106, asymmetry measures are calculated between an
individual's two eyes. The asymmetry may utilize any portion of the
coefficients and phase values generated in block 104 or may utilize
other information. In some embodiments, asymmetry measures between
portions of the same eye are calculated. The phase values generated
in block 104 can be useful in many different forms. As shown in
block 106, the absolute value of the phase of each of the Fourier
components can be taken to create a measure based on absolute phase
deviance.
[0028] In block 108, a discriminant analysis is performed on the
Fourier coefficients, phase values, and asymmetry measurements to
create measures to aid in the diagnosis of glaucoma. In some
embodiments, a stepwise discriminant analysis is used so that
variables that contribute significantly to the function are
included.
[0029] In block 110, the linear discriminant function generated in
block 108 is used to evaluate whether an individual's data is
normal or not. In some embodiments, block 110 corresponds to using
the result of the discriminant analysis to aid in the diagnosis of
glaucoma.
[0030] In block 112, the results of the linear discriminant
function are evaluated by calculating a receiver operator
characteristic (ROC) curve. This is useful in some embodiments when
determining linear discriminant functions to be used with future
populations. ROC curves can be compared against each other to
determine linear discriminant functions to be used. In other
embodiments, actions represented by this block are not performed.
For example, when the linear discriminant function has been chosen
and is not being evaluated for efficacy, the actions of block 112
can be omitted.
[0031] FIG. 2 shows a block diagram of an apparatus in accordance
with various embodiments of the invention. Apparatus 200 includes
data receiver 210, processor 220, memory device 240, and display
230. Data receiver 210 is a device capable of receiving data
representing the shape or thickness of a surface. For example, data
receiver 210 may be an interface circuit that interfaces to an SLP
or OCT device as previously described. Also for example, data
receiver 210 may be an SLP or OCT device that measures NFL
thickness data directly.
[0032] Processor 220 is a computing device that is capable of
performing the method embodiments of the present invention. For
example, referring now back to FIG. 1, processor 220 may perform
the actions listed in the various blocks. In a clinical setting,
processor 220 may receive NFL thickness data from data receiver
210, perform Fourier analysis on the data, and then apply measures
and discriminants to the data to aid in the diagnosis of glaucoma.
In a research setting, processor 220 may also perform a linear
discriminant analysis to generate linear discriminant functions,
and then generate receiver operator curves (ROC) to display the
efficacy of the various discriminants.
[0033] Processor 220 may be any type of processor suitable to
perform actions to support the operation of apparatus 200. For
example, processor 220 may be a microprocessor, a microcontroller,
or the like. Also for example, processor 220 may be a hardware
controller or a collection of hardware controllers that perform
specific tasks. Also, in some embodiments, processor 220 is
embedded in a measurement device such as an SLP or OCT device.
Further, processor 220 may be a commercially available processor
embedded in a computer, such as a personal computer (PC).
[0034] Display 230 is an apparatus capable of displaying results
from processor 220. In some embodiments of the invention, display
230 is a video monitor capable of graphically displaying
information regarding measurements, Fourier analysis, and the like.
In other embodiments, display 230 is a simpler device that displays
the results of applying discriminant functions. For example,
display 230 may be a small printer that prints out numerical
information relating to results. In some embodiments, the display
provides a diagnosis based on the preceding analysis. In other
embodiments, display 230 does not provide a diagnosis, but instead
provides information to aid in the diagnosis, and the diagnosis is
left to personnel operating apparatus 200.
[0035] Memory device 240 represents an article that includes a
machine-accessible medium. For example, the memory may represent
any one or more of the following: a hard disk, a floppy disk,
random access memory (RAM), read only memory (ROM), flash memory,
CDROM, or any other type of article that includes a medium readable
by a machine. The memory may store instructions for performing the
execution of the various method embodiments of the present
invention. The memory may also store NFL thickness data, results
from discriminant analysis, and other information in support of the
various method embodiments of the present invention.
[0036] This disclosure, while it applies to any type of shape,
describes in detail multiple methodologies of detecting glaucoma
through shape analysis of nerve fibers in the eye. The remainder of
this disclosure is organized to describe: 1) different nerve fiber
layer measurements; 2) Fourier analysis of those measurements; and
3) different measures and discriminants that utilize information
generated by the Fourier analysis. Experimental results are also
described from studies performed using a few representative
embodiments of the invention.
Nerve Fiber Layer Measurements
[0037] Since glaucoma leads to characteristic damage of the retinal
ganglion cells, strategies for the detection of glaucoma often
focus on detecting the functional or structural changes associated
with ganglion cell disruption. Computer-assisted imaging
technologies for detection of the neural structural defects
associated with glaucoma that are based on topography of the
retinal nerve fiber layer (NFL) offer promise for detecting and
assessing glaucomatous disruption. Multiple clinical tools are
available for objective quantitative assessment of the NFL.
Examples include, but are not limited to, a scanning laser
polarimeter such as the GD x device (Laser Diagnostic Technologies,
Inc. San Diego, Calif.) or the ocular coherence tomograph (OCT,
Zeiss-Humphrey, Dublin Calif.). Other devices capable of measuring
the shape of a surface area may also be used.
[0038] The scanning laser polarimeter infers NFL thickness based on
optical properties of the NFL. When polarized light is projected
through a birefringent structure such as the nerve fiber layer, it
undergoes a shift, or retardation, in polarization. The amount of
shift is dependent upon the thickness of the structure, with a
greater shift implying a higher thickness. The polarimeter measures
retardation to provide an indication of the thickness of the nerve
fiber layer around the optic disc after compensating for
retardation from other structures (e.g., cornea). The ocular
coherence tomograph infers thickness based on measurements of the
time-course of back-scattered light.
[0039] Nerve fiber layer measurements can be made on an entire eye,
or on a subset of an eye. For example, in some embodiments of the
invention, nerve fiber thickness data is collected and analyzed for
each hemiretina (superior and inferior) of an eye. In other
embodiments of the invention, nerve fiber thickness data is
collected and analyzed for the entire retina of each eye. In still
further embodiments, measurement data representing both hemiretina
and the entire eye are collected and analyzed.
[0040] Some embodiments of the present invention utilize
measurement information gathered for a circular region defined by a
ring around the optic disc. The ring may have any diameter, and
also may be divided into angular sectors for analysis. Various
embodiments of the invention utilizing rings about the optic disc
are described further below.
Entire Retina NFL Measurement
[0041] FIGS. 3 and 5 show two characteristic "double hump" patterns
of NFL thickness values obtained from a circular ring around the
retina. The double-hump pattern represents a full 360.degree.
pattern around the retina. As shown in FIG. 3, 16 NFL thickness
values are plotted as a function of degrees around the retina. The
first 180.degree. represent the superior hemiretina, and the last
180.degree. represent the inferior hemiretina. The superior
hemiretina and the inferior hemiretina are divided by a vertical
dashed line.
[0042] As shown in FIG. 5, 32 NFL thickness values are plotted to
show the superior and inferior hemiretinas. FIG. 5 is also labeled
to indicate the temporal and nasal portions of the plot. The
double-hump pattern of FIG. 5 is not plotted as a function of
degrees, but instead is shown as a function of distance numbers
used in the Fourier analysis (described below). Notwithstanding the
labeling of the plot in FIG. 5, the double hump pattern represents
substantially 360.degree. around the retina.
[0043] Both FIGS. 3 and 5 show NFL thickness data gathered for the
circular region defined by a 1.7 disc diameter ring (a distance of
0.85 disc diameters from the disc margin). The ring in FIG. 3 is
divided into 16 angular sectors of 22.5.degree. each. The ring in
FIG. 5 is divided into 32 angular sectors of 11.25.degree. each.
These diameters and numbers of angular sectors are representative,
and are not a limitation of the invention. For example, in some
embodiments of the invention, rings having a diameter other than
1.7 disc are utilized. Also for example, although FIGS. 3 and 5
have 16 and 32 samples, respectively, any number of samples can be
taken, and therefore, the ring can be divided into any number of
angular sectors.
Hemiretina NFL Measurement
[0044] As described above with reference to the entire retina NFL
measurement, the hemiretina NFL measurement corresponds to
substantially 180.degree. of arc around the optic disc. FIG. 3
shows the superior and inferior hemiretinas divided by a dashed
line. In some embodiments of the invention, hemiretina data is
collected separately for each hemiretina in each eye. In other
embodiments, NFL thickness data is collected for the full retina as
described above, and the analysis is performed on a
hemiretina-by-hemiretina basis.
[0045] Analysis on separate hemiretinas rather than on the full
360.degree. pattern can be useful in part because the superior and
inferior nerve bundles are not located exactly 180.degree. apart
and also in part because this allows asymmetrical (superior vs.
inferior) changes within a retina to be assessed.
[0046] Each data point shown in FIGS. 3 and 5 can be a result of a
single measurement, or can be an average of multiple measurements.
For example, within an angular sector bounded by the edges of a
ring, multiple measurements may be made and averaged. In some
embodiments of the invention, the averaging of multiple
measurements occurs in the device performing the measurement, such
as an SLP device or OCT device, and in other embodiments, the
measuring device provides raw data having multiple measurements
which are then averaged separately.
Fourier Analysis of Nerve Fiber Layer Measurements
[0047] Various embodiments of the present invention emphasize the
holistic shape of the pattern of NFL thickness variation across
many locations, rather than local thickness per se. Fourier
analysis is used to take into account the whole shape of the
bi-modal "double-hump" distribution of nerve fiber layer thickness
around the optic disc. The analysis is based on many local
measurements and emphasizes the relative differences. (such as rate
of change) between areas. That is, rather than emphasizing
thickness itself, which is known to vary widely among normals,
measures made possible by Fourier analysis emphasize
characteristics that typify the shape of the distribution of
thickness in normals, while substantially ignoring variation of
absolute thickness level. Various embodiments of the present
invention achieve this by analyzing the pattern of the distribution
of the NFL thickness and comparing this pattern in normals and
glaucoma patients to aid in distinguishing glaucomatous eyes from
normal eyes.
[0048] Fourier analysis is a mathematical procedure whereby a
complex waveform pattern can be broken down (i.e., analyzed) into a
set of harmonically related sine-waves of specified frequencies,
amplitudes, and phases, which, when added together point for point,
reproduce the original waveform. The lowest frequency component,
referred to herein as the "fundamental," corresponds to the basic
form of the pattern, and the harmonics are sine-waves-whose
frequencies are integer multiples of the fundamental (e.g., the
second harmonic has a frequency that is two times that of the
fundamental).
[0049] The textbook Wavelets and Subband Coding by Martin Vetterli
and Jelena Kovacevic (Prentice Hall PTR, Upper Saddle River, N.J.),
which is hereby incorporated by reference, teaches in detail the
computation of fast Fourier Transform (pp. 337-341).
[0050] Any waveform (e.g. the "double hump" pattern of nerve fiber
distribution) can be analyzed as a sum of sinusoidal components of
various frequencies through Fourier analysis. Fourier analysis
provides the amplitude and phase of the sinusoidal components that
(additively) constitute a particular waveform; that is, a linear
analysis in which the composite pattern is broken down into a set
of components which, when added point by point, yield the original
pattern.
Fourier Analysis of Hemiretinal Measurements
[0051] FIG. 4 shows the result of a Fourier analysis of the
superior hemiretina NFL thickness data shown to the left of the
dashed vertical line in FIG. 3. In FIG. 4, the eight data points
from the superior hemiretina are plotted as solid points. The dark
solid line plotted with the data points shows the resultant curve
fit to the data by Fourier analysis. The curve is formed by the
point-to-point addition of the other four curves plotted: the
fundamental frequency (Ff); the second harmonic (F2), which is at
twice the frequency of the fundamental; the third harmonic (F3),
which is at three times the frequency of the fundamental, and the
DC component.
[0052] As shown in FIG. 4, Fourier analysis was applied to one half
of the "double hump" pattern of thickness values (i.e., a
hemiretina) obtained at a disc diameter of 1.7. Thus, the Fourier
analysis provides a set of sine-waves such that the amplitude of
each indicates the relative contribution of that spatial scale to
the shape of the composite curve fitting the NFL measurements (see
FIG. 3). For example, if the NFL pattern was a pure sine-wave, the
curve would be perfectly described by the fundamental and all other
coefficients would be zero. Since the NFL shape is not sinusoidal,
the harmonics serve to "shape" the fundamental so that the
composite curve shape matches the NFL pattern. FIG. 4 illustrates
how the addition of the Fourier components shape the composite
curve to better fit the data.
[0053] Each of the curves shown in FIG. 4 includes attributes such
as amplitude and phase. For example, the amplitude of the
fundamental is the greatest, and the amplitudes of the second and
third harmonics are smaller than the amplitude of the fundamental.
Phase values generated by the Fourier analysis described phase
offsets of each of the Fourier components. For example, the
fundamental component and the first and second harmonics each have
different phase offsets. The various Fourier component amplitude
and phase values are useful when generating discriminant functions
to aid in the diagnosis of disease.
Fourier Analysis of Entire Retina Measurements
[0054] FIG. 6 shows the result of a Fourier analysis of the entire
double-hump pattern shown in FIG. 5. The sum of the first three
Fourier components of FIG. 6 are shown superimposed on the
double-hump pattern in FIG. 5. As higher order Fourier components
are summed in to the curve shown in FIG. 5, a better curve fit is
obtained. The Fourier analysis of the double-hump patter of FIG. 5
yields nine waveforms in FIG. 6: the fundamental component, and the
first eight harmonic components. Each component is characterized by
attributes such as amplitude and phase. As described further below,
the attributes of the various Fourier components can be utilized
can be useful to aid in the diagnosis of disease.
Measures and Discriminants
[0055] Various embodiments of the present invention use
discriminant measures based on the attributes of Fourier components
for the superior and inferior hemiretinas and also full retinas of
the left and right eyes for both the normals and glaucoma
patients.
Discriminant Measures Include:
[0056] Ffund: the amplitude of the fundamental Fourier component;
roughly analogous to the amplitude of the basic sinusoidal nature
of the NFL distribution in a hemiretina. [0057] Fsum: the sum of
amplitudes of all or a subset of Fourier components (fundamental
and harmonics); roughly corresponding to the composite amplitude of
the whole shape of the periodic waveform in a hemiretina. [0058]
FasymW/in: the superior/inferior intraocular asymmetry within an
eye in the amplitude of the Fsum measure. To avoid including eyes
with an asymmetry due to a greater than normal amount of nerve
fibers, some embodiments of the invention also require that at
least one Fsum value (either superior or inferior value) also
satisfies a threshold value. [0059] FasymBet: the interocular
asymmetry of the overall thickness (Fourier DC component). Like the
FasymW/in measure, some embodiments of the invention also require
that one of the Ffund values is also below a specified value.
[0060] FasymCoeff: an asymmetry measure calculated based on a ratio
between the two eyes for each coefficient value from a full retinal
Fourier analysis. The amplitude of each coefficient from one eye is
compared against the amplitude of the corresponding coefficient
from the other eye to yield asymmetry measures that quantify the
interocular asymmetry of each Fourier component separately. [0061]
Logical OR Composite: to form a composite measure, any or all of
the above measures may be combined by a "logical OR". That is, an
eye may be classified as glaucomatous if it is deviant on any one
of the contributing single measures. [0062] Linear Discriminant
Analysis: a linear discriminant function is generated using the
Fourier amplitude and phase coefficients obtained from a full
retinal analysis. Any combination of amplitude coefficients, phase
values, interocular asymmetry measures, and intraocular asymmetry
measures are entered into a discriminant analysis, and linear
discriminant functions of the significant terms are generated.
[0063] In some embodiments, the discriminant analysis is a stepwise
discriminant analysis.
[0064] As illustrated in the following experimental studies, the
above measures and discriminants can be combined and modified in
the various embodiments of the invention. As previously stated, the
present invention encompasses the methods and apparatus used to
generate the appropriate measures and discriminants, and also
encompasses the use of the various measures and discriminants in a
clinical setting to aid in diagnoses.
Experimental Studies
[0065] Multiple studies are now described. Each study utilizes
various embodiments of the invention to aid in the diagnosis of
glaucoma. The patient populations within the studies are described,
as are the types of measurements made on those patients. Also
described are the Fourier analyses applied, and the various
measures and discriminants used to aid in the diagnosis.
Study Number 1: Fourier Analysis of Hemiretinal Data from a Single
Population
[0066] Sixty eight volunteers (thirty four normals and thirty four
glaucomatous patients) from the Louisville, Ky. area participated
in the study. Both eyes were used in the analysis for all the
normals and glaucoma patients. Ages ranged from 40-84 with a mean
age of 53.5 (s.d.=9.8) for normals and 69.4 (s.d.=10.4) for
glaucoma patients. All participants underwent ophthalmologic exams
and no evidence of ocular pathology was found for the normals. All
thirty four patients were diagnosed as having open angle glaucoma
with no other ocular pathology. All glaucoma patients had been
drawn from a glaucoma clinic and had some degree of glaucomatous
field loss in both eyes. Visual fields were tested within 6 months
of imaging with the Humphrey perimeter in sixty two eyes using the
30-2 threshold test. For the remaining six eyes with advanced
glaucoma, the visual field testing had been performed with the 10-2
threshold test. An eye was classified as having glaucoma if the
Humphrey glaucoma hemifield test was abnormal, the corrected
pattern standard deviation was outside 95% normal limits, or a
repeated cluster of 3 or more points was depressed at the 5% level
on the pattern deviation plot. The average mean deviation (MD) for
the 62 glaucomatous eyes who performed the 30-2 test was -8.9 dB.
Only eight out of those 62 eyes had MD greater than -15 dB and were
thus classified as having advanced glaucoma along with the six eyes
that performed 10-2 visual field test (14 eyes total).
Retinal Imaging
[0067] Retinal thickness measures were obtained using a scanning
laser polarimeter (Laser Diagnostic Technologies, Inc., San Diego).
A retardation map was created by directing the scanning beam across
a 15.degree. field of view, producing a 256.times.256 pixel image
approximately centered on the optic disc. Three images of each eye
were obtained and averaged to create baseline images that were
analyzed further. Degrees of retardation were converted into
microns by a conversion factor of 1.degree. of retardation per 7.4
microns. However, these thickness estimates are referred to herein
as "thickness units" (t.u.) in part because of the approximate
nature of the conversion to microns.
Data Analysis
[0068] Thickness values (in t.u.) were obtained by extracting a ten
pixel wide circular region ("ring") at a specific distance from the
optic disc and dividing it into sixteen angular sectors. Each of
the sixteen sectors was 22.5.degree. wide and labeled with the
lower bound (0.degree. to 337.degree.). Thickness values were
averaged for the pixels within each sector of the ring creating one
thickness estimate for each region. Thus, for each 10 pixel wide
ring, there were sixteen measures of thickness for each eye, with 8
from the superior hemiretina and 8 from the inferior hemiretina.
Although sixteen sectors were chosen for analysis, any number of
sectors can be analyzed by dividing the ring differently. The
Fourier measures were calculated for the circular region defined by
the 1.7 disc diameter ring (a distance of 0.85 disc diameters from
the disc margin). The Fourier analyses were also performed on
diameters other than 1.7 and were found to produce similar
results.
[0069] Fourier analysis was applied to one half of the "double
hump" pattern of thickness values (i.e., a hemiretina) obtained at
a disc diameter of 1.7. Thus, each Fourier analysis provided a set
of sine-waves such that the amplitude of each indicated the
relative contribution of that spatial scale to the shape of the
composite curve fitting the NFL measurements. For example, if the
NFL pattern was a pure sine-wave, the curve would be perfectly
described by the fundamental and all other coefficients would be
zero. Since the NFL shape is not sinusoidal, the harmonics serve to
"shape" the fundamental so that the composite curve shape matches
the NFL pattern.
[0070] The pattern of the thickness measurements for the eight
values of a hemifield were analyzed to get the Fourier amplitude
coefficients (calculated as the square root of the sum of the
squares of the real and imaginary part of each Fourier
coefficient). The FFT analysis of a sequence of 8 numbers results
in five unique coefficient values: the direct current (DC) value
(analogous to the overall mean or a scaling factor), the
fundamental, the second harmonic, the third harmonic and the fourth
harmonic components. Various discriminate measures were developed
based on these Fourier coefficients for the superior and inferior
hemiretinas of the left and right eyes for both the normals and
glaucoma patients. The associated ROC curves and the area under the
curves were calculated by stepping through each cutoff value and
noting the number of correct detections and false alarms for our
sample.
[0071] We used several measures that were based on the Fourier
coefficients: the amplitude of the fundamental Fourier component
(F.sub.fund), roughly analogous to the amplitude of the basic
sinusoidal nature of the NFL distribution in a hemiretina;
F.sub.sum, the sum of amplitudes of the first four Fourier
components (fundamental, second harmonic, third harmonic and fourth
harmonic), roughly corresponding to the composite amplitude of the
whole shape of the periodic waveform in a hemiretina; the
superior/inferior asymmetry within an eye in the amplitude of the
F.sub.sum measure, F.sub.asymW/in; and the interocular asymmetry of
the overall thickness (Fourier DC component), F.sub.asymBet. (In
this latter measure, the DC measure was used rather than the
F.sub.sum measure in order to capitalize on the greater inter-eye
asymmetry of the measure.) To form composite measures, these
measures were combined by a "logical OR". That is, an eye was
classified as glaucomatous if it was deviant on any one of the
contributing single measures. To determine the cut-off values for
dividing glaucoma from normal, the value under consideration (or
values on each single measure in the case of a composite measure)
was systematically adjusted until the value that maximized the sum
of the sensitivity and specificity was found.
[0072] The results below describe the analysis which proceeded in
several steps. First the utility of the fundamental component of
the Fourier analysis alone were assessed, and then the additional
Fourier components and the symmetry comparisons (hemifield or eyes)
of Fourier measures were sequentially added to consider different
composite measures. For each of these measures the sensitivity and
specificity was calculated and then compared for the different
measures as well as for standard GDx.RTM. analysis (see Table 1).
TABLE-US-00001 TABLE 1 Diagnostic Measure Sensitivity (%)
Specificity (%) Fundamental alone (F.sub.fund) 77% 91% Sum of the
Fourier 75% 91% amplitudes alone (F.sub.sum) F.sub.fund and
F.sub.sum combined 84% 90% F.sub.fund, F.sub.sum, F.sub.asymW/in,
and 96% 90% F.sub.asymBet combined GDx .RTM. number 85% 91%
[0073] Independent-groups t-tests were used to compare the groups
of normal and glaucoma subjects whenever possible (i.e., for the
Ffund and Fsum measures). The Fourier coefficients for the glaucoma
patients and normal subjects were compared statistically with a
2.times.4.times.2.times.2 mixed design MANOVA (patient category x
Fourier coefficients x eye x hemiretina). A significance level of
0.05 was adopted for all tests and the Wilks F values were used to
determine significance.
Results
[0074] The first measure, the amplitude of the fundamental
component (F.sub.fund) is closely related to the size of the peak
of the NFL distribution in a hemiretina but emphasizes (sinusoidal)
shape as well. The smaller of the two hemiretina values was
selected for this measure (F.sub.fund) for each eye. The
distribution of F.sub.fund for the normal and glaucomatous eyes are
shown in the left panel of FIG. 7 at 702. As shown in FIG. 7, the
amplitude of the fundamental is typically larger for normals than
patients (t.sub.(270)=13.86, p<0.0001). The ability of this
Fourier measure to discriminate eyes with glaucoma from normal eyes
was evaluated. FIG. 8 shows the Receiver Operator Characteristic
(ROC) curve for this measure. The area under this ROC curve was
0.91 and the sensitivity and specificity values were 77% and 91%,
respectively, with a cut-off of 10 (i.e., an eye was defined as
glaucomatous if the Fourier fundamental was less than or equal to
10).
[0075] As previously described, loss of nerve fibers will not only
lead to the reduction of the amplitude of the fundamental
component, but also to the reduction of the amplitudes of higher
frequency components (in the Fourier analysis, these components
effectively "shape" the main hump provided by the fundamental
component). A more detailed measure of shape characteristics, as
indicated by Fourier analysis, is the sum of the amplitudes of the
fundamental, second, third, and fourth harmonic coefficients. This
sum (F.sub.sum) was calculated separately for both hemiretinas and
again, the smaller of the two values (for a given eye) was chosen
for analysis. The distribution of F.sub.sum for the normal eyes and
eyes with glaucoma is shown in the middle panel of FIG. 7 at 704.
As with F.sub.fund, F.sub.sum for normals was significantly higher
than that for the patients (t.sub.(270)=12.73, p<0.0001). A ROC
curve (FIG. 9) was obtained for F.sub.sum to assess its ability to
distinguish between normal and glaucomatous eyes. The area under
the ROC curve was 0.90. Sensitivity and specificity values were 75%
and 91%, respectively, with the cut-off of 15.7.
[0076] Since deviant values on either one of these measures
(F.sub.fund or F.sub.sum) might be indicative of NFL abnormalities,
it is possible that the combination of these two measures would
result in an increase in the sensitivity and specificity values
obtained relative to those obtained with either measure alone. With
the cut-offs set, previously, 10 for the Ffund component and 15.7
for F.sub.sum, the sensitivity and specificity with this combined
(by a logical OR) measure was 84% and 90%, respectively. The
improvement shown by this combined measure is achieved in part
because certain glaucoma patients not detected by one single
measure alone are detected by the other measure when they are
combined.
[0077] The sum of Fourier amplitudes, F.sub.sum, reflects deviation
from the normal shape of the hemifield's distribution of thickness
values including aspects such as width of the peak and trough,
shape of the rising and falling sides of the peak and trough,
peak-to-trough amplitude, and others. However, since glaucoma often
affects one eye more than the other, or one hemifield more than the
other, consideration of an asymmetry in the Fourier measures
between hemiretinas or between eyes, might improve their ability to
separate glaucomatous eyes from normal eyes. Both types of
asymmetry were considered, combining the Fourier measures
(F.sub.fund and F.sub.sum) with both a measure of symmetry between
the hemifields of an eye and a measure of asymmetry between the
eyes.
[0078] To reflect superior/inferior asymmetry within an eye,
F.sub.asymW/in was used, which represents the difference between
the Fourier sum (F.sub.sum) for the superior and inferior
hemiretinas. Looking at hemifield asymmetry in this way is similar
to an anatomical analogue of the standard perimetry hemifield test.
Eyes were defined as glaucomatous if the F.sub.asymW/in was greater
than or equal to 6.2, but to avoid including eyes with an asymmetry
due to a greater than normal amount of nerve fibers, at least one
F.sub.sum value (either superior or inferior value) was required to
be less than 19. In other words, this second constraint helps to
limit the evaluation of asymmetries to eyes with an asymmetrical
disruption of nerve fiber thickness loss, as opposed to an
asymmetrical distribution of "extra" fibers in normals.
[0079] The other type of asymmetry, asymmetry between eyes, was
also incorporated into the composite measure. The difference
between the DC component of corresponding hemiretinas of the right
and left eyes was calculated and the larger (either superior or
inferior) difference (F.sub.asymBet) was taken. The DC measure was
used because its asymmetry was greater than for the other Fourier
measures. The DC, F.sub.fund and F.sub.sum measures differed by
7.7%, 21.5% and 13.9%, respectively, on average, between the two
eyes for normals, and 15.2%, 69.3%, and 35.3% respectively for
glaucoma patients. Both eyes were classified as glaucomatous if the
F.sub.asymBet was greater than 6 and one of their F.sub.fund values
(from either the superior or inferior hemiretina, depending on
which comparison was used for the F.sub.asymBet measure) was less
than 11. Again, as with the asymmetry measure within eyes, this
second constraint was incorporated to make sure the asymmetry was
due to nerve fiber loss and not due to the "extra" fibers of one
eye of a normal pair of companion eyes associated with the normal
variation commonly found between eyes of normal individuals.
[0080] When all four of the Fourier measures were combined (with
the same cut-offs as reported above) into one composite measure the
sensitivity and specificity for the subjects was 96% and 90%
respectively. That is, the combined measure considers asymmetry
between the eyes (F.sub.asymBet), between hemiretinas within eyes
(F.sub.asymW/in), as well as the amplitude of the fundamental
(F.sub.fund), and the sum of the amplitudes of the fundamental,
first, second, third and fourth harmonics (F.sub.sum), and yields
good sensitivity and specificity.
[0081] The group of patients used in the current study contained
some patients (14 eyes) who had advanced field loss (see Methods).
With the exclusion of these fourteen eyes, the sensitivity was
94.4%, indicating that the high sensitivity is not due to the
sample characteristics.
[0082] The GDx.RTM. software (LDT, Inc.) calculates fifteen
measures based on the retardation map created by the scanning laser
polarimeter. One of those fifteen is a GDx.RTM. number calculated
by training a neural network based on the retardation values at all
pixels. The GDx.RTM. number ranges from 0 to 100, with 100
indicating advanced glaucoma and 0 indicating completely normal.
The distribution of the GDx.RTM. number for the normals and
patients in the sample is shown in the right panel of FIG. 7 at 706
for comparison. Like the F.sub.fund and F.sub.sum measures, the
GDx.RTM. number for the normals was significantly less than that
for the patients (t.sub.(134)=-10.55, p<0.0001). An ROC curve
was constructed based on the GDx.RTM. number (FIG. 10). The area
under the ROC curve for the GDx.RTM. number was 0.90. Taking a low,
medium, and high cutoff of 15, 20, and 25 respectively, the
sensitivity and specificity for the GDx.RTM. number is 96% and 63%,
76.5% and 89.7%, and 75% and 85% respectively. Thus, the
sensitivity and specificity values obtained with the combination of
the four Fourier measures was higher than that obtained with the
GDx.RTM. number as well as these single Fourier measures used
alone.
[0083] To consider the variation in shape of the nerve fiber
thickness pattern (i.e., the "double-hump" pattern) further, the
relation of the amplitudes of the fundamental, second, third and
fourth harmonic components was considered for each individual. The
pattern of the amplitudes of the four Fourier components (FIG. 11)
differed significantly between the normal and glaucoma patient
groups (F.sub.(3,64)=22.4, p<0.001). No significant differences
were found for either which hemifield was considered (superior or
inferior), or which eye was considered (p>0.05). To quantify the
relation of these coefficients, the values of the fundamental,
second, third, and fourth harmonics for the patient and normal
groups were normalized by dividing each by the magnitude of the
fundamental for the respective group. This procedure shows that for
the normal group the four Fourier harmonics were in the ratio of
1.0:0.23:0.14:0.10, whereas for the glaucoma patients they were in
the ratio of 1.0:0.38:0.22:0.16. This suggests that aside from the
fundamental, the primary difference between the glaucoma and normal
groups is the 2.sup.nd harmonic.
Discussion
[0084] A new approach to analyzing polarimetry data is able to
discriminate effectively between normal and glaucomatous NFL
patterns. The approach utilizes Fourier analysis of thickness
measurements and creates a measure that emphasizes the pattern of
the distribution of thickness measurements as defined by certain
Fourier components. This measure is a composite of: the power of
the fundamental Fourier component; the summed power of the first
four Fourier components; superior/inferior asymmetry in the
distribution of thicknesses (i.e., in the Fourier harmonics,
F.sub.sum); and the interocular asymmetry in mean level of the
Fourier waveform (the DC component). Deviance on any one component
classifies the eye as glaucomatous. The Fourier measures
(F.sub.fund and F.sub.sum combined) appear to offer some
improvement over the standard GDx.RTM. number; they also offer the
advantage that they are direct (i.e., a direct analysis of the
single factor, shape) and easier to interpret than a weighted
combination of 15 factors (as in the calculation of the GDx
Number). In addition, the Fourier measures offer promise for future
development as a normal "template" shape and a metric of deviation
from this normals' template which might allow the detection of
local NFL defects. When combined with measures of intraocular and
interocular asymmetry of the Fourier measures, the sensitivity and
specificity are further improved.
[0085] A limitation of this study arises from the fact that the
sensitivity and specificity values were determined from the same
sample used to derive the criteria for discrimination. Thus the
high sensitivity and specificity values likely overestimate the
discriminability of the measures on an independent sample. In order
to measure the degree of overestimation or bias, a split-half
technique was performed where the sample was divided into 2 groups
(each group contained half the normals and half the patients
randomly selected). In the first sample the cut-offvalues were
optimized and a sensitivity and specificity of 94% and 91% were
achieved, respectively (new cut-off values were 6 for F.sub.fund, 8
for F.sub.sum, 6 for F.sub.asymW/in with a F.sub.sum below 20, and
3 for FasymBet with a F.sub.fund below 11). These cut-offs were
then used on the second sample yielding a sensitivity and
specificity of 94% and 88% respectively. This split-half technique
suggests the discriminability of the Fourier measures is robust
over independent samples.
[0086] The methods in this study were applied to each eye to
characterize eyes as normal or glaucomatous. An alternative method
would have been to apply the measures to each pair of eyes and to
classify individuals as normal or glaucoma patients, especially
since one measure (F.sub.asymBet) is based on an interocular
comparison. A per individual method would, of course, generally
tend to increase sensitivity of a measure given the additional
chance (two eyes) to obtain a deviant value. Indeed, applying the
same measures with the same cut-offs on a per individual, as
opposed to per eye, basis resulted in a sensitivity and specificity
of 94% and 82%, respectively, of the composite measure.
[0087] Another important factor to consider is the severity of
glaucomatous disruption present in the patients used in the
analysis. For example, in this study, only fourteen eyes were
classified as having advanced field loss. Furthermore the
sensitivity for patients with early or moderate field loss (i.e.,
with those 14 advanced eyes omitted) was still 94%. Thus, the
Fourier measures performed well with patients with mild or moderate
field loss.
Study Number 2: Applying Measures from One Population to Other
Populations
[0088] This study examines the question of whether measures derived
from one data set retain their robustness when applied to new data
sets obtained from different devices and populations. A
discriminant analysis of the Fourier coefficients is also used.
[0089] This study evaluates two analytical methods that utilize
Fourier analysis of NFL thickness estimates to detect glaucoma.
Both methods were optimized for each of three individual data sets.
The robustness of these methods was then assessed by applying each
method (with optimized parameters or cut-offs from each data set)
to the other data sets. Results are compared to other analysis
methods.
[0090] Three independent data sets were utilized. Normals had
undergone ophthalmologic examinations which revealed no evidence of
glaucomatous optic neuropathy. Glaucoma patients were defined
according to standard conventions by their referring
ophthalmologist. Both eyes were used for all individuals. The three
data sets were from the University of Louisville (UL), the
University of Washington (UW), and Laser Diagnostic Technologies
(LDT). The distribution of normal eyes to glaucomatous eyes were as
follows: TABLE-US-00002 University of 60 Normal Eyes 54
Glaucomatous Eyes Louisville: University of 82 Normal Eyes 76
Glaucomatous Eyes Washington: Laser Diagnostic 238 Normal Eyes 212
Glaucomatous Eyes Technologies:
Retinal Imaging
[0091] NFL thickness estimates were obtained using three scanning
laser polarimeters from Laser Diagnostic Technologies, Inc., San
Diego, Calif. The thickness estimates obtained were then subjected
to a Fourier analysis. Input data were the thickness estimates for
32 sectors located radially around the optic disc at 1.7 disc
diameters (standard software output). The Fourier coefficients were
then utilized in the two analysis methods (outlined below).
[0092] NFL thickness data for the three sample populations are
shown in FIGS. 12, 13, and 14. FIG. 15 shows the normal eyes from
all three data sets, and FIG. 16 shows the glaucomatous eyes from
all three data sets.
Data Analysis
[0093] This study utilized a logical OR measure of discriminants as
well as a stepwise discriminant analysis. The logical OR measure
uses the Fourier amplitude coefficients taken by performing an
analysis by hemiretina (superior and inferior separately). The
coefficients are then combined in different ways to create several
tests that are applied to the data in a manner similar to that
discussed in study number 1, above. Cut-off values were optimized
and the tests were combined by a logical OR operation; an
individual was then classified as glaucomatous if they `fail` any
of the established tests (i.e., their measure is below cut-off
value). There are four tests that were combined by logical OR:
[0094] Test 1. F1: This test uses the fundamental coefficient by
itself
[0095] Test 2. Fsum: This test sums all the coefficient values
together.
[0096] Test 3. FasymW: This test uses the difference between the
superior retina and inferior retina Fsum values (as long as one
value is above a specified amount).
[0097] Test 4. FasymB: This test takes the difference between the
DC value between the two eyes (for both superior retina and
inferior retina). A second stipulation is that at least one
Fundamental measure was above a criterion amount).
[0098] The linear discriminant function approach uses the Fourier
amplitude and phase coefficients obtained from a full retinal
analysis (not hemiretina). An asymmetry measure is calculated based
on a ratio between the two eyes for each coefficient value. For
example, the ratio of second harmonic amplitude coefficients is
referred to as "asyF2Aplitude," and the ratio of ninth harmonic
phase coefficients is referred to as "asyF9Phase." Then all
coefficients and phase values along with the between-eye asymmetry
measures were entered into a stepwise discriminant analysis and
linear discriminant functions of the significant terms were
generated. A ROC curve analysis was then performed using these
functions.
[0099] The linear discriminant functions generated for each data
set are as follows: TABLE-US-00003 UL Data: 0.27 (F2.sub.Amplitude)
+ 0.29 (F5.sub.Amplitude) + 0.90 (F9.sub.Amplitude) + 0.46
(asyF7.sub.Phase) - 0.88 (asyF9.sub.Phase) -
0.60(asyF10.sub.Amplitude) + 1.03(asyF11.sub.Amplitude) -
0.76(asyF12.sub.Amplitude) - 0.79 (asyF13.sub.Phase) +
1.31(asyF15.sub.Amplitude) - 3.84 UW Data: 0.22 (F2.sub.Amplitude)
+ 0.46 (F5.sub.Amplitude) - 0.17 (F8.sub.Phase) - 1.68
(asyF2.sub.Amplitude) + 4.18 (asyF2.sub.Phase) + 0.77
(asyF7.sub.Amplitude) - 0.91 (asyF9.sub.Amplitude) +
0.86(asyF11.sub.Amplitude) - 0.53 (asyF13.sub.Amplitude) - 2.64 LDT
Data: -0.02 (DC.sub.Amplitude) - 0.37 (F1.sub.Phase) + 0.21
(F2.sub.Amplitude) + 0.14 (F3.sub.Amplitude) + 0.26
(F5.sub.Amplitude) - 0.17(F5.sub.Phase) + 0.50(F9.sub.Amplitude) -
1 .25(asyF2.sub.Amplitude) - 0.43(asyF3.sub.Amplitude) - 0.35
(asyF4.sub.Phase) - 0.48 (asyF9.sub.Phase) + 0.33
(asyF11.sub.Amplitude) + 0.41(asyF13.sub.Amplitude) - 0.42
(asyF15.sub.Phase) + 0.24 (asyF16.sub.Phase) - .79
Results
[0100] Table 2 shows the area under the ROC curve and the SEM for
both Fourier analysis methods as well as for various other methods
commonly used. Analysis methods used are the GDx `Number` (based on
a neural network analysis of the output parameters), a linear
discriminant function based on three GDx output parameters
(Weinreb, Zangwill, et al., Archives of Ophthalmology 1998), and
four modulation parameters (Xu, Chen, Chen, Takahashi, Wang, and
Mills, Journal of Glaucoma 1998). For the logical OR measure, a ROC
analysis cannot be performed because different tests are combined
in the procedure; therefore sensitivity and specificity are
reported with specificity set at 90%. TABLE-US-00004 TABLE 2 Table
2. Comparisons from ROC Analysis for Different Analytical
Approaches GDx Number UCSD-LDF Fourier-LDF MxModS MxModI ReModS
ReModI Fourier-OR UL .929 (.022) .938 (.02) .989 (.007) .907 (.025)
.908 (.027) .901 (.028) .899 (.031) 95/90 UW .80 (.034) .825 (.032)
.937 (.018) .81 (.035) .818 (.033) .818 (.035) .819 (.033) 48/90
LOT .892 (.015) .892 (.016) .943 (.011) .886 (.016) .798 (.021)
.887 (.016) .809 (.021) 72/90
[0101] Table 3 shows the area under the ROC curve and the SEM for
the Fourier-based linear discriminant functions (LDF). Each LDF was
derived using a given data set, and then these formulas were
applied to each of the other data set to test for robustness.
Shaded cells indicate the cases when a particular function was
applied to the data set from which it was derived (same as Table
2.). TABLE-US-00005 TABLE 3 Table 3. Fourier LDF's Applied Across
Data Sets Database LDF was derived from UL UW LDT Database UL
##STR1## .842 (.032) .886 (.027) LDF UW .782 (.038) ##STR2## .807
(.035) applied to LDT .849 (.018) .816 (.02) ##STR3##
[0102] Table 4 shows the sensitivity and specificity for the
Fourier analysis method based on the logical OR combinatory
procedure. The optimum cut-offs were-derived using a given data
set, and then these cut-offs were applied to each of the other data
set to test for robustness. Shaded cells indicate the cases when
the cut-offs were applied to the data set from which they were
derived (same as Table 1.). TABLE-US-00006 TABLE 4 Table 4. Fourier
logical OR tests applied to other data sets Database cut-off values
were derived from UL UW LDT Database UL ##STR4## 61/72 81/80 OR
test UW 66/94 ##STR5## 59/93 applied to LDT 84/89 49/83
##STR6##
Discussion
[0103] The LDF based on Fourier measures relies on a variety of
"shaping" components in addition to the F2.sub.Amplitude (which is
similar to a peak-trough modulation measure). Prominent factors
such as F5.sub.Amplitude and F9.sub.Amplitude, and phase factors
specify the shape of the NFL humps and can be important in this
analysis. Asymmetries of phase and amplitude can also be important,
presumably due to the asymmetric disruption of NFL shape in
glaucoma.
[0104] The use of Fourier analysis to quantify the shape of the NFL
estimates can provide the basis for new methods that successfully
discriminate normal from glaucomatous eyes. These methods can be
more effective than current methods commonly used (average ROC area
for the three data sets using Fourier-LDF was 0.96, see Table
2).
Study Number 3: Applying Measures to Two Data Sets from One
Population
[0105] This study evaluates NFL thickness estimates taken from two
retinal imaging devices for the use of accurately discriminating
between healthy and glaucomatous eyes. The same subjects were
imaged with the two retinal imaging devices. One eye was randomly
imaged from each subject. Of 80 total eyes, 38 were healthy, and 42
were glaucomatous. Glaucoma was defined by repeatable abnormal
visual fields (SAP) with either GHT results outside normal limits
(99%) or a CPSD outside normal limits (95%).
Retinal Imaging
[0106] Estimates of NFL thickness were obtained using a scanning
laser polarimeter (SLP) available from Laser Diagnostic
Technologies, Inc., San Diego, Calif., and an ocular coherence
tomograph (OCT) available from Zeiss-Humphrey, Dublin, Calif. Both
devices provide NFL thickness estimates around the optic disc,
however they operate using very different principles. The scanning
laser polarimeter uses polarized light to infer thickness as
measured by the change in polarization (retardation). Polarimetry
measures used for this analysis were split up into 32 sectors
located radially around the optic disc at a distance of 1.7 disc
diameters.
[0107] The OCT infers thickness based on measurements of the
time-course of backscattered light. Thickness estimates at 100
angles around a ring (3.4 mm diameter centered on the disc) were
obtained which were then split into 12 sectors located radially
around the optic disc.
[0108] NFL thickness estimates taken by the SLP device (also
referred to as GDx data) are shown in FIG. 17, and NFL thickness
estimates taken by the OCT device are shown in FIG. 18. FIG. 19
shows normalized data plotted together from both devices. The
values were normalized for each curve by subtracting the curve's
mean from each point, and then dividing the resulting value by the
maximum value for each device.
Data Analysis
[0109] The same analysis and procedures were applied to both data
types (GDx and OCT). First, a fast Fourier transform (FFT) was
performed on the thickness estimates for each individual. The
Fourier analysis provides coefficients indicating the amplitude and
phase of each sine-wave frequency component that make up the
wave-form being analyzed (the shape of the curve plotting the
original data as a function of angle). The number of resulting
components is equal to half the number of data points being
analyzed.
[0110] A discriminant analysis was performed on the Fourier
coefficients (amplitude and phase values) and linear discriminant
functions were generated. This analysis was performed separately on
the GDx data and the OCT data. The discriminant functions were as
follows:
[0111] GDx Data: 0.26 (F2.sub.Amplitude)+1.37
(F12.sub.Amplitude)+0.68 (F12.sub.Phase)-1.91
(F13.sub.Amplitude)+0.80 (F14.sub.Phase)-5.94
[0112] OCT Data: 0.04 (DC)+0.08 (F2.sub.Amplitude)+0.68
(F3Phase)-6.58
Results
[0113] Sensitivity and specificity values for various methods of
analysis are shown in Table 5. Specificity was set at a minimum of
70% (left side), and at a minimum of 90% (right side). For the GDx
data, analysis methods used for comparison purposes are the GDx
`Number` (based on a neural network analysis of the output
parameters), a linear discriminant function based on three GDx
output parameters (Weinreb, Zangwill, et al., Archives of
Ophthalmology 1998), and a discriminant function derived from the
Fourier coefficients. For the OCT data, analysis methods used for
comparison purposes are mean thickness, thickness at the inferior
sector, and a discriminant function based on Fourier coefficients.
TABLE-US-00007 TABLE 5 Table from ROC data (Spec > 70%) Table
from ROC data (Spec > 90%) Number UCSD-LDF Fourier-LDF Number
UCSD-LDF Fourier-LDF GDx 63/71 71/71 97/71 GDx 47/90 47/90 82/90
Mean Inferior Fourier-LDF Mean Inferior Fourier-LDF OCT 92/71 97/71
95/71 OCT 71/90 58/90 76/90
[0114] FIGS. 20, 21, and 22 show ROC plots for GDx data, and FIGS.
23, 24, and 25 show ROC plots for OCT data. The area under the
curve and the standard error of the mean are given in each figure.
For both GDx and OCT data, the Fourier analysis of the present
invention provides superior results.
Discussion
[0115] OCT and GDx devices produce estimates of NFL thickness that
differ is several important ways (FIGS. 17 and 18), yet both are
analyzed effectively with the shape-based analysis provided by the
methods and apparatus of the present invention. (See Table 5 and
FIGS. 20-25).
[0116] Discriminant analysis of the Fourier components yields
sensitivity and specificity that is higher than that obtained by
analysis methods commonly used for either GDx or OCT devices.
[0117] It is to be understood that the above description is
intended to be illustrative, and not restrictive. Many other
embodiments will be apparent to those of skill in the art upon
reading and understanding the above description. The scope of the
invention should, therefore, be determined with reference to the
appended claims, along with the full scope of equivalents to which
such claims are entitled.
* * * * *