A framework for Level Set segmentation of volumetric datasets
2001 (English)In: IEEE Volume Graphics 01,2001, 2001, 159- p.Conference paper (Refereed)
This paper presents a framework for extracting surface models from a broad variety of volumetric 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. 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 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. Users can combine these methods in different ways and thereby access a wide range of functionalities, several of which are described in this paper and demonstrated on noisy volume data.
Place, publisher, year, edition, pages
2001. 159- p.
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-36965Local ID: 33166OAI: oai:DiVA.org:liu-36965DiVA: diva2:257814
IEEE Volume Graphics 01, 21-22 June 2001, New York, USA