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dc.contributor.authorLee, Ping-Changen_US
dc.contributor.authorChang, Hsiu-Mingen_US
dc.contributor.authorLin, Chih-Yangen_US
dc.contributor.authorChiang, Ann-Shynen_US
dc.contributor.authorChing, Yu-Taien_US
dc.date.accessioned2014-12-08T15:10:16Z-
dc.date.available2014-12-08T15:10:16Z-
dc.date.issued2009en_US
dc.identifier.issn1609-0985en_US
dc.identifier.urihttp://hdl.handle.net/11536/7840-
dc.description.abstractA semiautomatic method, based on the gradient flow (GVF) snake, to construct the neuronal structure from 3D confocal microscopic images is presented. A single neuron can be labeled by using green fluorescent protein (GFP) such that the 3D neuronal structure can be visualized in a stack of confocal microscopic images. To construct the neuronal structure, we traced a target fiber by providing a rough initial path to approximate that fiber. The path is then deformed under the gradient vector field to converge to the centerline of the fiber by using the GVF snake method. Using our developed software, a neuronal sturctural can be efficiently reconstructed and the tracing results can be reduced to the tree structure of the neuron. Given the tree structure of the neuron, quantitative information can be derived and further analysis can be carried out.en_US
dc.language.isoen_USen_US
dc.subjectConfocal microscopyen_US
dc.subjectGreen fluorescent protein (GFP)en_US
dc.subjectNeuron tracingen_US
dc.subjectGradient vector flow (GVF) snakeen_US
dc.subjectTree structureen_US
dc.titleConstructing Neuronal Structure from 3D Confocal Microscopic Imagesen_US
dc.typeArticleen_US
dc.identifier.journalJOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERINGen_US
dc.citation.volume29en_US
dc.citation.issue1en_US
dc.citation.spage1en_US
dc.citation.epage6en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000266547700001-
dc.citation.woscount4-
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