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Image and Volume Segmentation by Water Flow

Image and Volume Segmentation by Water Flow
Image and Volume Segmentation by Water Flow
A general framework for image segmentation is presented in this paper, based on the paradigm of water flow. The major water flow attributes like water pressure, surface tension and capillary force are defined in the context of force field generation and make the model adaptable to topological and geometrical changes. A flow-stopping image functional combining edge- and region-based forces is introduced to produce capability for both range and accuracy. The method is assessed qualitatively and quantitatively on synthetic and natural images. It is shown that the new approach can segment objects with complex shapes or weak-contrasted boundaries, and has good immunity to noise. The operator is also extended to 3-D, and is successfully applied to medical volume segmentation.
62-74
Liu, Xin U.
dbdb088d-dabe-4e87-95bb-e57e81fc78da
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Liu, Xin U.
dbdb088d-dabe-4e87-95bb-e57e81fc78da
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Liu, Xin U. and Nixon, Mark S. (2007) Image and Volume Segmentation by Water Flow. Proc USVC 2007/ LNCS, United States. pp. 62-74 .

Record type: Conference or Workshop Item (Other)

Abstract

A general framework for image segmentation is presented in this paper, based on the paradigm of water flow. The major water flow attributes like water pressure, surface tension and capillary force are defined in the context of force field generation and make the model adaptable to topological and geometrical changes. A flow-stopping image functional combining edge- and region-based forces is introduced to produce capability for both range and accuracy. The method is assessed qualitatively and quantitatively on synthetic and natural images. It is shown that the new approach can segment objects with complex shapes or weak-contrasted boundaries, and has good immunity to noise. The operator is also extended to 3-D, and is successfully applied to medical volume segmentation.

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Published date: 2007
Venue - Dates: Proc USVC 2007/ LNCS, United States, 2007-01-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 265673
URI: http://eprints.soton.ac.uk/id/eprint/265673
PURE UUID: 946a5cf9-8dff-4ac8-9367-216b381115fc
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 07 May 2008 10:36
Last modified: 20 Jul 2019 01:28

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