Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chen, CM | en_US |
dc.contributor.author | Lu, HHS | en_US |
dc.contributor.author | Chen, YL | en_US |
dc.date.accessioned | 2014-12-08T15:41:22Z | - |
dc.date.available | 2014-12-08T15:41:22Z | - |
dc.date.issued | 2003-02-01 | en_US |
dc.identifier.issn | 0167-8655 | en_US |
dc.identifier.uri | http://dx.doi.org/10.1016/S0167-8655(02)00175-7 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/28146 | - |
dc.description.abstract | Ultrasound images are inherently difficult to analyze due to their echo texture, speckle noise and weak edges. Taking into account these characteristics, we present a new region-based approach for ultrasound image segmentation. It is composed of two primary algorithms, discrete region competition and weak edge enhancement. The discrete region competition features four techniques, region competition, statistical modeling of speckle, early vision modeling, and discrete concepts. In addition, to prevent regions from leaking out of the desired area across weak edges, edges located on the slowly varying slope are enhanced according to their position on the slope and the length of the slope. This new approach has been implemented and verified on clinical ultrasound images. (C) 2002 Elsevier Science B.V. All rights reserved. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | ultrasound image segmentation | en_US |
dc.subject | speckle | en_US |
dc.subject | early vision | en_US |
dc.subject | region segmentation | en_US |
dc.title | A discrete region competition approach incorporating weak edge enhancement for ultrasound image segmentation | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1016/S0167-8655(02)00175-7 | en_US |
dc.identifier.journal | PATTERN RECOGNITION LETTERS | en_US |
dc.citation.volume | 24 | en_US |
dc.citation.issue | 4-5 | en_US |
dc.citation.spage | 693 | en_US |
dc.citation.epage | 704 | en_US |
dc.contributor.department | 統計學研究所 | zh_TW |
dc.contributor.department | Institute of Statistics | en_US |
dc.identifier.wosnumber | WOS:000180256900005 | - |
dc.citation.woscount | 2 | - |
Appears in Collections: | Articles |
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