Biomedical Engineering Reference
In-Depth Information
Chapter 8
Level Set Segmentation of
Biological Volume Datasets
David Breen
1
, Ross Whitaker
2
, Ken Museth
3
, and Leonid Zhukov
4
8.1 Introduction
This chapter addresses the common problem of building meaningful 3D models
of complex structures from noisy datasets generated from 3D imaging devices.
In certain circumstances such data can be visualized
directly
[1-4]. While direct
techniques can provide useful insights into volume data, they are insufficient for
many problems. For instance, direct volume rendering techniques typically do
not remove occluding structures, i.e., they do not allow one to “peel back” the
various layers of the data to expose the inner structures that might be of interest.
They also do not generate the models needed for quantitative study/analysis of
the visualized structures. Furthermore, direct visualization techniques typically
do not perform well when applied directly to noisy data, unless one filters the
data first. Techniques for filtering noisy data are abundant in the literature, but
there is a fundamental limitation—filtering that reduces noise tends to distort
the shapes of the objects in the data. The challenge is to find methods which
present the best trade-off between fidelity and noise.
Level set segmentation relies on a surface-fitting strategy, which is effective
for dealing with both small-scale noise and smoother intensity fluctuations in
1
Department of Computer Science, Drexel University, Philadelphia, PA 19104, USA
2
School of Computing, University of Utah, Salt Lake City, UT 84112, USA
3
Department of Science and Technology, Linkoeping University, 601 74 Norrkoeping,
Sweden
4
Department of Computer Science, California Institute of Technology, Pasadena, CA 91125,
USA
415
