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dc.contributor.authorSoelter, Jan
dc.contributor.authorSchumacher, Jan
dc.contributor.authorSpors, Hartwig
dc.contributor.authorSchmuker, Michael
dc.date.accessioned2017-07-20T16:05:03Z
dc.date.available2017-07-20T16:05:03Z
dc.date.issued2014-09-01
dc.identifier.citationSoelter , J , Schumacher , J , Spors , H & Schmuker , M 2014 , ' Automatic segmentation of odor maps in the mouse olfactory bulb using regularized non-negative matrix factorization ' , Neuroimage , vol. 98 , pp. 279-288 . https://doi.org/10.1016/j.neuroimage.2014.04.041
dc.identifier.issn1053-8119
dc.identifier.otherPURE: 10469289
dc.identifier.otherPURE UUID: 3ebd01a2-fdb0-495b-a978-1a0f28ec5b56
dc.identifier.otherPubMed: 24769181
dc.identifier.otherScopus: 84902962422
dc.identifier.urihttp://hdl.handle.net/2299/19022
dc.description© 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-SA license (http://creativecommons.org/licenses/by-nc-sa/3.0/).
dc.description.abstractSegmentation of functional parts in image series of functional activity is a common problem in neuroscience. Here we apply regularized non-negative matrix factorization (rNMF) to extract glomeruli in intrinsic optical signal (IOS) images of the olfactory bulb. Regularization allows us to incorporate prior knowledge about the spatio-temporal characteristics of glomerular signals. We demonstrate how to identify suitable regularization parameters on a surrogate dataset. With appropriate regularization segmentation by rNMF is more resilient to noise and requires fewer observations than conventional spatial independent component analysis (sICA). We validate our approach in experimental data using anatomical outlines of glomeruli obtained by 2-photon imaging of resting synapto-pHluorin fluorescence. Taken together, we show that rNMF provides a straightforward method for problem tailored source separation that enables reliable automatic segmentation of functional neural images, with particular benefit in situations with low signal-to-noise ratio as in IOS imaging.en
dc.format.extent10
dc.language.isoeng
dc.relation.ispartofNeuroimage
dc.rights/dk/atira/pure/core/openaccesspermission/open
dc.subjectAlgorithms
dc.subjectAnimals
dc.subjectBrain Mapping
dc.subjectImage Processing, Computer-Assisted
dc.subjectMice
dc.subjectOdors
dc.subjectOlfactory Bulb
dc.subjectOptical Imaging
dc.subjectSmell
dc.titleAutomatic segmentation of odor maps in the mouse olfactory bulb using regularized non-negative matrix factorizationen
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Computer Science
dc.description.versiontypeFinal Published version
dcterms.dateAccepted2014-04-12
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1016/j.neuroimage.2014.04.041
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeopenAccess


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