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Of the many different approaches to global optimisation we have
investigated two strategies and combined them with a simple but fast
local optimisation method to produce a hybrid optimisation method.
The two strategies are: searching and multi-start optimisation.
Our hybrid optimisation method (also described in [12])
is specifically designed for the problem at hand, using prior
knowledge about the transformation parameters and typical data size
(FOV, voxel size, etc.) to help make the method efficient. The method
cannot guarantee that the global solution is found, but then neither
can any other global optimisation method given a finite amount of
time. Generally, only statistical ``guarantees'' are given, and these
often require excessive run-times in order to be met. In contrast,
our method is designed to give a reliable estimate of the global
minimum given some time restriction (in our case, less than one hour
on a moderately-powered standard workstation; e.g. registering two
mm images typically takes 15 minutes on a 500MHz
Pentium III).
The method still uses a local optimisation method with a
multi-resolution framework, and these are described in the next two
sections, followed by descriptions of the global search and
multi-start optimisation strategies employed.
Subsections
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Previous: Joint Histogram Apodization
Peter Bannister
2002-05-03