Academic
Publications
Segmentation of Biological Volume Datasets Using a Level-Set Framework

Segmentation of Biological Volume Datasets Using a Level-Set Framework,Ross Whitaker,David Breen,Ken Museth,Neha Soni

Segmentation of Biological Volume Datasets Using a Level-Set Framework   (Citations: 22)
BibTex | RIS | RefWorks Download
This paper presents a framework for extracting surface models from a broad variety of volume datasets. These datasets are produced from standard D imaging devices, and are all noisy samplings of complex biological struc- tures 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 deforma- tions alone are not sufficient, they must be combined with powerful initialization techniques in order to produce successful segmentations. Our level set segmenta- tion 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.
Conference: Volume Graphics - VG , 2001
Cumulative Annual
Sort by: