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Article: Using nonlinear diffusion and mean shift to detect and connect cross-sections of axons in 3D optical microscopy images

TitleUsing nonlinear diffusion and mean shift to detect and connect cross-sections of axons in 3D optical microscopy images
Authors
Issue Date2008
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/media
Citation
Medical Image Analysis, 2008, v. 12 n. 6, p. 666-675 How to Cite?
AbstractThe morphology of neuronal axons has been actively investigated by researchers to understand functionalities of neuronal networks, for example, in developmental neurology. Today's optical microscope and labeling techniques allow us to obtain high-resolution images about axons in three dimensions (3D), however, it remains challenging to segment and reconstruct the 3D morphology of axons. These include differentiating adjacent axons and detecting the axon branches. In this paper we present a method to track axons in 3D by identifying cross-sections of axons on 2D images and connecting the cross-sections over a series of 2D images to reconstruct the 3D morphology. The method can separate adjacent axons and detect the split and merge of axons. The method consists of three steps, modified nonlinear diffusion to remove noise and enhance edges in 2D, morphological operations to detect edges of the cross-sections of axons in 2D, and mean shift to track the cross-sections of axons in 3D. Performance of the method is demonstrated by processing real data acquired by confocal laser scanning microscopy. © 2008 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/156232
ISSN
2015 Impact Factor: 4.565
2015 SCImago Journal Rankings: 2.048
ISI Accession Number ID
Funding AgencyGrant Number
HKU10206889.
Harvard NeuroDiscovery Center
Harvard Medical School
Functional and Molecular Imaging Center
Department of Radiology, Brigham and Women's Hospital, Boston, MA
Funding Information:

The work of S.P. Yung was supported by a HKU Grant code 10206889. The work of X. Xu and S.T.C. Wong was supported by a grant from Harvard NeuroDiscovery Center, Harvard Medical School, and Functional and Molecular Imaging Center, Department of Radiology, Brigham and Women's Hospital, Boston, MA.

References

 

DC FieldValueLanguage
dc.contributor.authorCai, Hen_US
dc.contributor.authorXu, Xen_US
dc.contributor.authorLu, Jen_US
dc.contributor.authorLichtman, Jen_US
dc.contributor.authorYung, SPen_US
dc.contributor.authorWong, STCen_US
dc.date.accessioned2012-08-08T08:40:57Z-
dc.date.available2012-08-08T08:40:57Z-
dc.date.issued2008en_US
dc.identifier.citationMedical Image Analysis, 2008, v. 12 n. 6, p. 666-675en_US
dc.identifier.issn1361-8415en_US
dc.identifier.urihttp://hdl.handle.net/10722/156232-
dc.description.abstractThe morphology of neuronal axons has been actively investigated by researchers to understand functionalities of neuronal networks, for example, in developmental neurology. Today's optical microscope and labeling techniques allow us to obtain high-resolution images about axons in three dimensions (3D), however, it remains challenging to segment and reconstruct the 3D morphology of axons. These include differentiating adjacent axons and detecting the axon branches. In this paper we present a method to track axons in 3D by identifying cross-sections of axons on 2D images and connecting the cross-sections over a series of 2D images to reconstruct the 3D morphology. The method can separate adjacent axons and detect the split and merge of axons. The method consists of three steps, modified nonlinear diffusion to remove noise and enhance edges in 2D, morphological operations to detect edges of the cross-sections of axons in 2D, and mean shift to track the cross-sections of axons in 3D. Performance of the method is demonstrated by processing real data acquired by confocal laser scanning microscopy. © 2008 Elsevier B.V. All rights reserved.en_US
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/mediaen_US
dc.relation.ispartofMedical Image Analysisen_US
dc.subject.meshAlgorithmsen_US
dc.subject.meshAnatomy, Cross-Sectional - Methodsen_US
dc.subject.meshAnimalsen_US
dc.subject.meshArtificial Intelligenceen_US
dc.subject.meshAxons - Ultrastructureen_US
dc.subject.meshImage Enhancement - Methodsen_US
dc.subject.meshImage Interpretation, Computer-Assisted - Methodsen_US
dc.subject.meshImaging, Three-Dimensional - Methodsen_US
dc.subject.meshMiceen_US
dc.subject.meshMice, Transgenicen_US
dc.subject.meshMicroscopy - Methodsen_US
dc.subject.meshPattern Recognition, Automated - Methodsen_US
dc.subject.meshPeripheral Nerves - Cytologyen_US
dc.subject.meshReproducibility Of Resultsen_US
dc.subject.meshSensitivity And Specificityen_US
dc.titleUsing nonlinear diffusion and mean shift to detect and connect cross-sections of axons in 3D optical microscopy imagesen_US
dc.typeArticleen_US
dc.identifier.emailYung, SP:spyung@hkucc.hku.hken_US
dc.identifier.authorityYung, SP=rp00838en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.media.2008.03.002en_US
dc.identifier.pmid18440853-
dc.identifier.scopuseid_2-s2.0-54249159267en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-54249159267&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume12en_US
dc.identifier.issue6en_US
dc.identifier.spage666en_US
dc.identifier.epage675en_US
dc.identifier.eissn1361-8423-
dc.identifier.isiWOS:000261295100003-
dc.publisher.placeNetherlandsen_US
dc.identifier.scopusauthoridCai, H=14420921700en_US
dc.identifier.scopusauthoridXu, X=7405293993en_US
dc.identifier.scopusauthoridLu, J=14421449500en_US
dc.identifier.scopusauthoridLichtman, J=7005493194en_US
dc.identifier.scopusauthoridYung, SP=7006540951en_US
dc.identifier.scopusauthoridWong, STC=12781047500en_US

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