Identifying White-Matter Fiber Bundles in DTI Data Using an Automated Proximity-Based Fiber-Clustering Method

We present a method for clustering diffusion tensor imaging (DTI) integral curves into anatomically plausible bundles. An expert rater evaluated the anatomical accuracy of the bundles. We also evaluated the method by applying an experimental cross-subject labeling method to the clustering results. W...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on visualization and computer graphics Vol. 14; no. 5; pp. 1044 - 1053
Main Authors:	Zhang, Song, Correia, Stephen, Laidlaw, David H.
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.09.2008 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Algorithms Anatomy Artificial Intelligence Brain - cytology Bundles cluster Cluster Analysis Clustering Clustering algorithms Clustering methods Clusters Diffusion Magnetic Resonance Imaging - methods Diffusion tensor imaging DT-MRI DTI Female Fibers Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Labeling Magnetic resonance imaging Male Marking Mathematical models Middle Aged Nerve Fibers, Myelinated - ultrastructure Optical fiber testing Pattern Recognition, Automated - methods Proximity Reproducibility of Results Robustness Sampling methods Sensitivity and Specificity Termination of employment DTI cluster DT-MRI clustering Diffusion Tensor Imaging
ISSN:	1077-2626, 1941-0506
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	We present a method for clustering diffusion tensor imaging (DTI) integral curves into anatomically plausible bundles. An expert rater evaluated the anatomical accuracy of the bundles. We also evaluated the method by applying an experimental cross-subject labeling method to the clustering results. We first employ a sampling and culling strategy for generating DTI integral curves and then constrain the curves so that they terminate in gray matter. We then employ a clustering method based on a proximity measure calculated between every pair of curves. We interactively selected a proximity threshold to achieve visually optimal clustering in models from four DTI datasets. An expert rater then assigned a confidence rating about bundle presence and accuracy for each of 12 target fiber bundles of varying calibers and type in each dataset. We then created a fiber bundle template to cluster and label the fiber bundles automatically in new datasets. According to expert evaluation, the automated proximity-based clustering and labeling algorithm consistently yields anatomically plausible fiber bundles on large and coherent clusters. This work has the potential to provide an automatic and robust way to find and study neural fiber bundles within DTI.
AbstractList	We present a method for clustering diffusion tensor imaging (DTI) integral curves into anatomically plausible bundles. An expert rater evaluated the anatomical accuracy of the bundles. We also evaluated the method by applying an experimental cross-subject labeling method to the clustering results. We first employ a sampling and culling strategy for generating DTI integral curves and then constrain the curves so that they terminate in gray matter. We then employ a clustering method based on a proximity measure calculated between every pair of curves. We interactively selected a proximity threshold to achieve visually optimal clustering in models from four DTI datasets. An expert rater then assigned a confidence rating about bundle presence and accuracy for each of 12 target fiber bundles of varying calibers and type in each dataset. We then created a fiber bundle template to cluster and label the fiber bundles automatically in new datasets. According to expert evaluation, the automated proximity-based clustering and labeling algorithm consistently yields anatomically plausible fiber bundles on large and coherent clusters. This work has the potential to provide an automatic and robust way to find and study neural fiber bundles within DTI. We present a method for clustering diffusion-tensor imaging (DTI) integral curves into anatomically plausible bundles. An expert rater evaluated the anatomical accuracy of the bundles. We also evaluated the method by applying an experimental cross-subject labeling method to the clustering results. Our approach is guided by assumptions about the proximity of fibers comprising discrete white-matter bundles and proceeds as follows: We first employ a sampling and culling strategy for generating DTI integral curves and then constrain the curves so that they terminate in gray matter. This approach seems likely to retain anatomically plausible fibers. We then employ a clustering method based on a proximity measure calculated between every pair of curves. We interactively selected a proximity threshold to achieve visually optimal clustering in models from four DTI data sets. An expert rater then assigned a confidence rating about bundle presence and accuracy for each of the 12 target fiber bundles of varying calibers and types (i.e., commissural, association, and projection) in each data set. The interactive clustering and evaluation information was incorporated to create a fiber-bundle template. We then used the template to cluster and label the fiber bundles automatically in new data sets. According to expert evaluation, the automated proximity-based clustering and labeling algorithm consistently yields anatomically plausible fiber bundles, although fiber bundles with smaller calibers and those that are not highly directionally coherent are identified with lower confidence. This work has the potential to provide an automatic and robust way to find and study neural fiber bundles within DTI. According to expert evaluation, the automated proximity-based clustering and labeling algorithm consistently yields anatomically plausible fiber bundles on large and coherent clusters. We present a method for clustering diffusion tensor imaging (DTI) integral curves into anatomically plausible bundles. An expert rater evaluated the anatomical accuracy of the bundles. We also evaluated the method by applying an experimental cross-subject labeling method to the clustering results. We first employ a sampling and culling strategy for generating DTI integral curves and then constrain the curves so that they terminate in gray matter. We then employ a clustering method based on a proximity measure calculated between every pair of curves. We interactively selected a proximity threshold to achieve visually optimal clustering in models from four DTI datasets. An expert rater then assigned a confidence rating about bundle presence and accuracy for each of 12 target fiber bundles of varying calibers and type in each dataset. We then created a fiber bundle template to cluster and label the fiber bundles automatically in new datasets. According to expert evaluation, the automated proximity-based clustering and labeling algorithm consistently yields anatomically plausible fiber bundles on large and coherent clusters. This work has the potential to provide an automatic and robust way to find and study neural fiber bundles within DTI.We present a method for clustering diffusion tensor imaging (DTI) integral curves into anatomically plausible bundles. An expert rater evaluated the anatomical accuracy of the bundles. We also evaluated the method by applying an experimental cross-subject labeling method to the clustering results. We first employ a sampling and culling strategy for generating DTI integral curves and then constrain the curves so that they terminate in gray matter. We then employ a clustering method based on a proximity measure calculated between every pair of curves. We interactively selected a proximity threshold to achieve visually optimal clustering in models from four DTI datasets. An expert rater then assigned a confidence rating about bundle presence and accuracy for each of 12 target fiber bundles of varying calibers and type in each dataset. We then created a fiber bundle template to cluster and label the fiber bundles automatically in new datasets. According to expert evaluation, the automated proximity-based clustering and labeling algorithm consistently yields anatomically plausible fiber bundles on large and coherent clusters. This work has the potential to provide an automatic and robust way to find and study neural fiber bundles within DTI.
Author	Zhang, Song Correia, Stephen Laidlaw, David H.
Author_xml	– sequence: 1 givenname: Song surname: Zhang fullname: Zhang, Song email: szhang@cse.msstate.edu organization: Mississppi State University, Mississippi State – sequence: 2 givenname: Stephen surname: Correia fullname: Correia, Stephen email: stephen_correia@brown.edu organization: Brown Medical School, Providence – sequence: 3 givenname: David H. surname: Laidlaw fullname: Laidlaw, David H. email: dhl@cs.brown.edu organization: Brown University, Providence
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/18599916$$D View this record in MEDLINE/PubMed
BookMark	eNqFkktv1DAUhSNURB-wY4eEIhawIcP1O94gtVNaRmoFiyksLSdxOq4Su40d1Pn3dZhSoBLMxg_5u-c-fPazHeedybKXCGYIgfyw_DY_nWGAcsbwk2wPSYoKYMB30hmEKDDHfDfbD-EKAFFaymfZLiqZlBLxvaxfNMZF266tu8y_r2w0xbmO0Qz5ia3SejS6pjMhty4_Xi7yYx11fhEmWLv8cIy-19E0-dfB39rexnVxpEO6_wwu5t0YktREn5u48s3z7Gmru2Be3O8H2cXJp-X8c3H25XQxPzwrasZYLCRuKtNK3DJScWKkJhRqXXFesSr1xyskuKRAa9rQlrUEypbqkmIpuBCmkuQg-7jRvR6r3jR1anHQnboebK-HtfLaqr9fnF2pS_9DYcGEKHkSeHcvMPib0YSoehtq03XaGT8GJYFwjARBW8lSMCAAfCrq7X9JLrHkINhWkFCKEZJ8K4iBEyBsKvLNI_DKj4NLX6BKjkFwwqe0r_8c2sO0fpklAe83QD34EAbT_kZATV5UkxfV5EXFcMLxI7y2UUfrp5Hb7l9BrzZB1hjzoE-pkJQxcgf_Q-j8
CODEN	ITVGEA
CitedBy_id	crossref_primary_10_1016_j_jneumeth_2020_108626 crossref_primary_10_1016_j_cag_2015_06_003 crossref_primary_10_1111_jon_12854 crossref_primary_10_1016_j_neuroimage_2010_07_038 crossref_primary_10_1109_TMI_2019_2932681 crossref_primary_10_1007_s11604_010_0460_y crossref_primary_10_1109_TVCG_2011_155 crossref_primary_10_1109_TVCG_2009_112 crossref_primary_10_1109_TVCG_2016_2582158 crossref_primary_10_1118_1_4811155 crossref_primary_10_1109_ACCESS_2020_2981451 crossref_primary_10_1016_j_neuroimage_2012_02_071 crossref_primary_10_1016_j_ins_2015_04_045 crossref_primary_10_1007_s10439_010_0073_8 crossref_primary_10_1016_j_compbiomed_2017_01_016 crossref_primary_10_1016_j_neuroimage_2020_116673 crossref_primary_10_1109_TMI_2009_2016337 crossref_primary_10_1109_TVCG_2012_150 crossref_primary_10_1111_cgf_142647 crossref_primary_10_1007_s12650_019_00552_x crossref_primary_10_1109_TVCG_2011_78 crossref_primary_10_1109_TVCG_2015_2403323 crossref_primary_10_1016_j_neuroimage_2013_04_066 crossref_primary_10_1109_TVCG_2013_2297914 crossref_primary_10_1109_TVCG_2019_2940935 crossref_primary_10_1016_j_knosys_2019_06_028 crossref_primary_10_1002_hbm_22051 crossref_primary_10_1007_s12021_022_09593_4 crossref_primary_10_1016_j_mri_2011_07_012 crossref_primary_10_1016_j_neuroimage_2022_119197 crossref_primary_10_1016_j_artmed_2016_09_003 crossref_primary_10_1007_s12021_015_9264_7 crossref_primary_10_1371_journal_pone_0083847 crossref_primary_10_3389_fnins_2016_00554 crossref_primary_10_1007_s10618_015_0408_z crossref_primary_10_1007_s12021_011_9118_x crossref_primary_10_1093_neuros_nyaa463 crossref_primary_10_1097_RMR_0000000000000212 crossref_primary_10_1109_TVCG_2011_82 crossref_primary_10_3389_fninf_2014_00087 crossref_primary_10_1109_TBME_2014_2360797 crossref_primary_10_1007_s12021_020_09488_2 crossref_primary_10_1016_j_compbiomed_2016_07_015 crossref_primary_10_21300_18_1_2016_31 crossref_primary_10_1109_TVCG_2015_2467204 crossref_primary_10_1016_j_artmed_2019_03_002 crossref_primary_10_1016_j_media_2013_01_004 crossref_primary_10_1371_journal_pone_0093344
Cites_doi	10.1007/978-3-540-73273-0_31 10.1006/nimg.2002.1136 10.1002/mrm.1910400117 10.1007/11566465_18 10.1016/S1361-8415(01)00036-6 10.1002/1522-2594(200010)44:4<625::AID-MRM17>3.0.CO;2-O 10.1142/S0218195995000064 10.1109/VISUAL.2005.1532779 10.1002/mrm.10415 10.1007/11866763_30 10.1109/ISBI.2004.1398545 10.1016/s0003-2670(00)82860-3 10.1016/S1361-8415(02)00053-1 10.1007/978-3-540-30135-6_45 10.1148/radiol.2301021640 10.1109/TVCG.2003.1260740 10.1109/42.906424 10.1007/11566465_24
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2008 2008 IEEE 2008
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2008 – notice: 2008 IEEE 2008
DBID	97E RIA RIE AAYXX CITATION CGR CUY CVF ECM EIF NPM 7SC 7SP 8FD JQ2 L7M L~C L~D 7QO 7TK FR3 P64 F28 7X8 5PM
DOI	10.1109/TVCG.2008.52
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology Research Abstracts Neurosciences Abstracts Engineering Research Database Biotechnology and BioEngineering Abstracts ANTE: Abstracts in New Technology & Engineering MEDLINE - Academic PubMed Central (Full Participant titles)
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional Engineering Research Database Biotechnology Research Abstracts Neurosciences Abstracts Biotechnology and BioEngineering Abstracts ANTE: Abstracts in New Technology & Engineering MEDLINE - Academic
DatabaseTitleList	Technology Research Database MEDLINE Technology Research Database Engineering Research Database Technology Research Database Technology Research Database MEDLINE - Academic
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE/IET Electronic Library url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1941-0506
EndPage	1053
ExternalDocumentID	PMC2757786 2320844741 18599916 10_1109_TVCG_2008_52 4479455
Genre	orig-research Research Support, U.S. Gov't, Non-P.H.S Research Support, Non-U.S. Gov't Journal Article Research Support, N.I.H., Extramural
GrantInformation_xml	– fundername: NIBIB NIH HHS grantid: EB004155 – fundername: NIBIB NIH HHS grantid: R01 EB004155 – fundername: NIMH NIH HHS grantid: R01 MH074368 – fundername: NINDS NIH HHS grantid: R01 NS052470
GroupedDBID	--- -~X .DC 0R~ 29I 4.4 53G 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 IEDLZ IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RNI RNS RZB TN5 VH1 AAYXX CITATION CGR CUY CVF ECM EIF NPM RIG 7SC 7SP 8FD JQ2 L7M L~C L~D 7QO 7TK FR3 P64 F28 7X8 5PM
ID	FETCH-LOGICAL-c555t-92dbef92f53b63e9a340cab66b5b1946b1769404c4d4f5f308f4a84297677eb93
IEDL.DBID	RIE
ISICitedReferencesCount	83
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000257371400006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1077-2626
IngestDate	Tue Sep 30 16:05:12 EDT 2025 Sat Sep 27 21:59:08 EDT 2025 Wed Oct 01 13:59:38 EDT 2025 Sun Sep 28 02:20:58 EDT 2025 Sun Sep 28 11:23:30 EDT 2025 Tue Oct 07 09:22:53 EDT 2025 Sun Nov 09 06:48:56 EST 2025 Mon Jul 21 05:36:26 EDT 2025 Sat Nov 29 07:51:32 EST 2025 Tue Nov 18 22:18:35 EST 2025 Wed Aug 27 01:47:16 EDT 2025
IsDoiOpenAccess	false
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	5
Keywords	DTI cluster DT-MRI clustering Diffusion Tensor Imaging
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c555t-92dbef92f53b63e9a340cab66b5b1946b1769404c4d4f5f308f4a84297677eb93
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Article-2 ObjectType-Feature-1
OpenAccessLink	http://doi.org/10.1109/TVCG.2008.52
PMID	18599916
PQID	862076365
PQPubID	23500
PageCount	10
ParticipantIDs	proquest_miscellaneous_20630351 pubmedcentral_primary_oai_pubmedcentral_nih_gov_2757786 proquest_miscellaneous_875030069 ieee_primary_4479455 proquest_journals_862076365 proquest_miscellaneous_69296075 pubmed_primary_18599916 crossref_primary_10_1109_TVCG_2008_52 proquest_miscellaneous_903621731 proquest_miscellaneous_34421196 crossref_citationtrail_10_1109_TVCG_2008_52
PublicationCentury	2000
PublicationDate	2008-09-01
PublicationDateYYYYMMDD	2008-09-01
PublicationDate_xml	– month: 09 year: 2008 text: 2008-09-01 day: 01
PublicationDecade	2000
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: New York
PublicationTitle	IEEE transactions on visualization and computer graphics
PublicationTitleAbbrev	TVCG
PublicationTitleAlternate	IEEE Trans Vis Comput Graph
PublicationYear	2008
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref12 ref23 ref15 Zhang (ref19) ref14 ref20 ref11 Duda (ref8) 2000 ref2 Zhang (ref22) ref1 ref17 ref16 Lee (ref10) ref18 ref7 ref9 ref4 ref3 ref6 ref5 Zhang (ref21) 2002
References_xml	– ident: ref11 doi: 10.1007/978-3-540-73273-0_31 – volume-title: technical report, Dept. of Computer Science, Brown Univ. year: 2002 ident: ref21 article-title: Hierarchical Clustering of Streamtubes – ident: ref5 doi: 10.1006/nimg.2002.1136 – ident: ref1 doi: 10.1002/mrm.1910400117 – volume-title: Pattern Classification year: 2000 ident: ref8 – ident: ref14 doi: 10.1007/11566465_18 – ident: ref9 doi: 10.1016/S1361-8415(01)00036-6 – ident: ref3 doi: 10.1002/1522-2594(200010)44:4<625::AID-MRM17>3.0.CO;2-O – start-page: 283 volume-title: Proc. Int’l Soc. for Magnetic Resonance in Medicine (ISMRM ’06) ident: ref10 article-title: Quantitative Tract-of-Interest Metrics for White-Matter Integrity Based on Diffusion Tensor MRI Data – ident: ref2 doi: 10.1142/S0218195995000064 – ident: ref13 doi: 10.1109/VISUAL.2005.1532779 – ident: ref7 doi: 10.1002/mrm.10415 – ident: ref15 doi: 10.1007/11866763_30 – volume-title: Proc. Int’l Soc. for Magnetic Resonance in Medicine (ISMRM ’05) ident: ref22 article-title: DTI Fiber Clustering and Cross-Subject Cluster Analysis – ident: ref6 doi: 10.1109/ISBI.2004.1398545 – ident: ref16 doi: 10.1016/s0003-2670(00)82860-3 – ident: ref18 doi: 10.1016/S1361-8415(02)00053-1 – ident: ref4 doi: 10.1007/978-3-540-30135-6_45 – ident: ref17 doi: 10.1148/radiol.2301021640 – ident: ref20 doi: 10.1109/TVCG.2003.1260740 – ident: ref23 doi: 10.1109/42.906424 – ident: ref12 doi: 10.1007/11566465_24 – volume-title: Proc. Int’l Soc. for Magnetic Resonance in Medicine (ISMRM) ident: ref19 article-title: Correlating DTI Fiber Clusters with White-Matter Anatomy
SSID	ssj0014489
Score	2.238898
Snippet	We present a method for clustering diffusion tensor imaging (DTI) integral curves into anatomically plausible bundles. An expert rater evaluated the anatomical... According to expert evaluation, the automated proximity-based clustering and labeling algorithm consistently yields anatomically plausible fiber bundles on... We present a method for clustering diffusion-tensor imaging (DTI) integral curves into anatomically plausible bundles. An expert rater evaluated the anatomical...
SourceID	pubmedcentral proquest pubmed crossref ieee
SourceType	Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	1044
SubjectTerms	Accuracy Algorithms Anatomy Artificial Intelligence Brain - cytology Bundles cluster Cluster Analysis Clustering Clustering algorithms Clustering methods Clusters Diffusion Magnetic Resonance Imaging - methods Diffusion tensor imaging DT-MRI DTI Female Fibers Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Labeling Magnetic resonance imaging Male Marking Mathematical models Middle Aged Nerve Fibers, Myelinated - ultrastructure Optical fiber testing Pattern Recognition, Automated - methods Proximity Reproducibility of Results Robustness Sampling methods Sensitivity and Specificity Termination of employment
Title	Identifying White-Matter Fiber Bundles in DTI Data Using an Automated Proximity-Based Fiber-Clustering Method
URI	https://ieeexplore.ieee.org/document/4479455 https://www.ncbi.nlm.nih.gov/pubmed/18599916 https://www.proquest.com/docview/862076365 https://www.proquest.com/docview/20630351 https://www.proquest.com/docview/34421196 https://www.proquest.com/docview/69296075 https://www.proquest.com/docview/875030069 https://www.proquest.com/docview/903621731 https://pubmed.ncbi.nlm.nih.gov/PMC2757786
Volume	14
WOSCitedRecordID	wos000257371400006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE/IET Electronic Library customDbUrl: eissn: 1941-0506 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014489 issn: 1077-2626 databaseCode: RIE dateStart: 19950101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB61FQc4lEd5hELxAU5gmsSv-NhuWUCiVQ8L2tvKThyxUkmq3QTBv2fsZEMXNQdukfwlsjKezOd45huA16YsMs21oyYXmmLEQ5cSRtC4RPZvcm5TF7qWfFEXF9l8ri934N1QC-OcC8ln7r2_DGf5RZ23_lfZMfdy6ELswq5SqqvVGk4McJuhu_xCRVNk6UOSuz6efZt87LImRWhek4nAirYiUWitchvL_DdZ8kb0md7_v3k_gP2eZZKTblk8hB1XPYJ7N7QHD-BHV6IbypxIaJNHz4PWJpn6JBJy2nr5hTVZVuRs9pmcmcaQkF9ATEVO2qZGqusKcrmqf_kaqd_0FONh0d1MJ1etF2Dw6PPQovoxfJ1-mE0-0b73As2FEA3VaWFdqdNSMCuZ04bxODdWSitsorm0iZKaxzznBS9FyeKs5CbD4KakUs5q9gT2qrpyz4DIxDLGZOE0S7i1yhalEzJzKT7JSBZH8HZjh0XeC5P7_hhXi7BBifXCG7DrlynSCN4M6OtOkGMEd-ANMWB6G0RwuDHxovfW9QJ3dTF-ZyWOvhpG0c382YmpXN2u8ak4UyaScQTj3KvlyXGERCoqkaJFQEYQmT9V9uLR4xDtKUeiGM7kabcw_76GfmFHoLaW7ADwUuLbI9Xye5AUT5XwQoLPb39ph3B3kyYTJy9gr1m17iXcyX82y_XqCL1xnh0Fb_wDo-sypQ
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB6VggQcyqM8QoH6ACdwm8Sv-NhuWVqxu-phQb1FduKIlUqCdhPU_ntsJ5t2UXPgFslfIiszznyOZ74B-KCKPJFUGqwyJrGNeHZJMcVwWFj2rzKqY-O7lkzEbJZcXMjzLfjc18IYY3zymTlwl_4sP6-yxv0qO6RODp2xe3CfURpHbbVWf2ZgNxqyzTAUOLY8vU9zl4fzH6Ovbd4k8-1rEuZ50UYs8s1V7uKZ_6ZL3oo_4yf_N_OnsNPxTHTUOsYz2DLlc3h8S31wF361Rbq-0An5Rnl46tU20dilkaDjxgkwrNCiRCfzM3SiaoV8hgFSJTpq6sqSXZOj82V15aqkrvGxjYh5ezMeXTZOgsGhp75J9Qv4Pv4yH53irvsCzhhjNZZxrk0h44IRzYmRitAwU5pzzXQkKdeR4JKGNKM5LVhBwqSgKrHhTXAhjJbkJWyXVWleA-KRJoTw3EgSUa2FzgvDeGJi-yTFSRjAp7Ud0qyTJncdMi5Tv0UJZeoM2HbMZHEAH3v071aSYwC36wzRYzobBLC3NnHarddVavd1of3Scju634_aheZOT1RpqmZln2pnSlg0jCDOKe0XbRjBLRnllqQFgAYQiTtXdvLRwxDpSEckiJ3Jq9Yxb15D59gBiA2X7QFOTHxzpFz89KLisWBOSvDN3S9tHx6ezqeTdHI2-7YHj9ZJM2H0FrbrZWPewYPsT71YLd_7NfkXb0s1BA
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Identifying+White-Matter+Fiber+Bundles+in+DTI+Data+Using+an+Automated+Proximity-Based+Fiber+Clustering+Method&rft.jtitle=IEEE+transactions+on+visualization+and+computer+graphics&rft.au=Zhang%2C+Song&rft.au=Correia%2C+Stephen&rft.au=Laidlaw%2C+David+H.&rft.date=2008-09-01&rft.issn=1077-2626&rft.volume=14&rft.issue=5&rft.spage=1044&rft.epage=1053&rft_id=info:doi/10.1109%2FTVCG.2008.52&rft_id=info%3Apmid%2F18599916&rft.externalDocID=PMC2757786
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1077-2626&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1077-2626&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1077-2626&client=summon