Abstract:
This paper presents a framework for extracting surface models from a
broad variety of volume datasets. These 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. However, level set deformations alone
are not sufficient, they must
be combined with powerful initialization techniques in order to
produce successful segmentations.
Our level set segmentation approach
consists of defining a set of suitable pre-processing techniques for
initialization and selecting/tuning different feature-extracting terms
in the level set algorithm. This collection of techniques forms a
toolkit that can be applied, under the guidance of a user, to
segment a variety of volumetric data.