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dc.contributor.authorFeng, Xu
dc.contributor.authorBeyazoglu, Turker
dc.contributor.authorHefner, Evan
dc.contributor.authorGurkan, Umut Atakan
dc.contributor.authorDemirci, Utkan
dc.date.accessioned2011-09-27T20:13:39Z
dc.date.available2011-09-27T20:13:39Z
dc.date.issued2011-06
dc.identifier.issn1937-3392
dc.identifier.urihttp://hdl.handle.net/1721.1/66084
dc.description.abstractCellular alignment plays a critical role in functional, physical, and biological characteristics of many tissue types, such as muscle, tendon, nerve, and cornea. Current efforts toward regeneration of these tissues include replicating the cellular microenvironment by developing biomaterials that facilitate cellular alignment. To assess the functional effectiveness of the engineered microenvironments, one essential criterion is quantification of cellular alignment. Therefore, there is a need for rapid, accurate, and adaptable methodologies to quantify cellular alignment for tissue engineering applications. To address this need, we developed an automated method, binarization-based extraction of alignment score (BEAS), to determine cell orientation distribution in a wide variety of microscopic images. This method combines a sequenced application of median and band-pass filters, locally adaptive thresholding approaches and image processing techniques. Cellular alignment score is obtained by applying a robust scoring algorithm to the orientation distribution. We validated the BEAS method by comparing the results with the existing approaches reported in literature (i.e., manual, radial fast Fourier transform-radial sum, and gradient based approaches). Validation results indicated that the BEAS method resulted in statistically comparable alignment scores with the manual method (coefficient of determination R2=0.92 [R superscript 2 = 0.92]). Therefore, the BEAS method introduced in this study could enable accurate, convenient, and adaptable evaluation of engineered tissue constructs and biomaterials in terms of cellular alignment and organization.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NIH R21 (AI087107))en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NIH R01 (AI081534))en_US
dc.description.sponsorshipWallace H. Coulter Foundationen_US
dc.description.sponsorshipCenter for Integration of Medicine and Innovative Technologyen_US
dc.description.sponsorshipUnited States. Army Medical Research and Materiel Commanden_US
dc.description.sponsorshipUnited States. Army. Telemedicine & Advanced Technology Research Centeren_US
dc.language.isoen_US
dc.publisherMary Ann Lieberten_US
dc.relation.isversionofhttp://dx.doi.org/10.1089/ten.tec.2011.0038en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceMary Ann Lieberten_US
dc.titleAutomated and Adaptable Quantification of Cellular Alignment from Microscopic Images for Tissue Engineering Applicationsen_US
dc.typeArticleen_US
dc.identifier.citationXu, Feng et al. “Automated and Adaptable Quantification of Cellular Alignment from Microscopic Images for Tissue Engineering Applications.” Tissue Engineering Part C: Methods 17, no. 6 (2011): 641-649. Copyright © 2011, Mary Ann Liebert, Inc.en_US
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technologyen_US
dc.contributor.approverDemirci, Utkan
dc.contributor.mitauthorDemirci, Utkan
dc.relation.journalTissue Engineering. Part C, Methodsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsXu, Feng; Beyazoglu, Turker; Hefner, Evan; Gurkan, Umut Atakan; Demirci, Utkanen
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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