Abstract:

This chapter describes level set techniques for extracting surface models from a
broad variety of biological volume datasets. These techniques have been incorporated
into a more general framework that includes other volume processing
algorithms. The volume datasets are produced from standard 3D imaging devices,
and are all noisy samplings of complex biological structures with boundaries
that have low and often varying contrasts. The level set segmentation
method, which is well documented in the literature, creates a new volume from
the input data by solving an initial value partial differential equation (PDE)
with user-defined feature-extracting terms. Given the local/global nature of
these terms, proper initialization of the level set algorithm is extremely important.
Thus, level set deformations alone are not sufficient, they must be
combined with powerful pre-processing and data analysis techniques in order
to produce successful segmentations. This chapter describes the pre-processing
and data analysis techniques that have been developed for a number of segmentation
applications, as well as the general structure of our framework. Several
standard volume processing algorithms have been incorporated into the framework
in order to segment datasets generated from MRI, CT and TEM scans.
A technique based on moving least-squares has been developed for segmenting
multiple non-uniform scans of a single object. New scalar measures have been
defined for extracting structures from diffusion tensor MRI scans. Finally, a
direct approach to the segmentation of incomplete tomographic data using density
parameter estimation is described. These techniques, combined with level
set surface deformations, allow us to segment many different types of biological
volume datasets.

Last modified on September 7, 2005.