Details of the iteration procedure
The inverse method uses an N-dimensional minimization algorithm
called AMOEBA [49]. This algorithm is based on
the downhill simplex method of [45]. The function
to be minimized is
 |
(6.1) |
where
Rdiffusedelta-E and
are the diffuse reflection and total transmission
as calculated with the delta-Eddington model. Only two measurements
are used because either the collimated transmission
Tcollmathrmmeas is known
and consequently the delta-Eddington optical depth
is known
 |
(6.2) |
or
gHG is known (from which g' can be derived). Thus the function
in Equation (6.1) is really a function of only two
variables.
The total light transmitted in the delta-Eddington model is
 |
(6.3) |
where is the diffuse transmission given by Equation (4.106).
It should be emphasized that the delta-Eddington collimated
transmission is not equal to the measured collimated transmission
(except in the special case when f=0 and ). In particular
 |
(6.4) |
The delta-Eddington collimated transmission is not physically
observable, but merely a mathematical finesse to improve the
accuracy of the approximation by treating some fraction of highly
forward scattered light as `collimated' light.
A Henyey-Greenstein shape may be imposed on the phase function
(to second order in Legendre polynomials--see Section 1.3.3)
by varying
gHG instead of g'. This places a restriction on the
relation between g' and f. This has the added advantage of removing
f as an unknown parameter. Currently no evidence for the shape
of a phase function at wavelengths other than 633nm exists,
and this restriction should be made with caution. If the Henyey-Greenstein
phase function is assumed then g' and f are calculated according
to Equations (1.4) and (1.5).
 |
(6.5) |
Finally, it is possible to vary g' independently of f, and omit
calculating a and
from the final values a' and
(which requires
knowledge of f), but such model dependent parameters have not
been found useful.
The minimization routine amoeba assumes the range over which
parameters may be varied is
to .
Unfortunately,
the anisotropy and albedo have fixed ranges. This is remedied
by transforming the albedo and anisotropy into a ``calculation
space.'' As acalc varies from
to
in the calculation
space, the actual albedo a varies from 0 to 1. The same transformation
is made on the anisotropy, since all tissues measured heretofore
have had positive anisotropies. The transformation function
is
 |
(6.6) |
where x represents either
gHG or a depending on the need. If
negative anisotropies are included, then the transformation function
for
gHG must be altered to vary from -1 to 1 as the calculation
parameter varies from
to .
Flowcharts for algorithms based on either known
gHG or known
are shown in Figures 6.4 and 6.5. These illustrate the changes
of variables which take place during each iteration of the method.
Starting values for an initial guess of a', ,
and g' maybe obtained
in a few different ways. First, Kubelka-Munk optical properties
may be calculated from the reflection and transmission and these
may be converted to transport optical properties. This turns
out to be not any better than just starting at fixed intermediate
values of the optical properties (a'=0.5, ,
and g'=0.2).
However, when calculating optical properties for a series of
different wavelengths, excellent starting values to use for the
next wavelength are the optical properties of the previous wavelength.
|