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dc.contributor.authorAlavi, A
dc.contributor.authorCavanagh, B
dc.contributor.authorTuxworth, G
dc.contributor.authorMeedeniya, A
dc.contributor.authorMackay-Sim, A
dc.contributor.authorBlumenstein, M
dc.contributor.editorRobert Kozma
dc.date.accessioned2017-05-03T16:59:18Z
dc.date.available2017-05-03T16:59:18Z
dc.date.issued2009
dc.date.modified2011-10-11T07:14:12Z
dc.identifier.isbn9781424435531
dc.identifier.refurihttp://www.ijcnn2009.com/
dc.identifier.doi10.1109/IJCNN.2009.5178740
dc.identifier.urihttp://hdl.handle.net/10072/28868
dc.description.abstractAccurate morphological characterization of the multiple neuronal classes of the brain would facilitate the elucidation of brain function and the functional changes that underlie neurological disorders such as Parkinson's diseases or Schizophrenia. Manual morphological analysis is very time-consuming and suffers from a lack of accuracy because some cell characteristics are not readily quantified. This paper presents an investigation in automating the classification of dopaminergic neurons located in the brainstem of the rodent, a region critical to the regulation of motor behaviour and is implicated in multiple neurological disorders including Parkinson's disease. Using a Carl Zeiss Axioimager Z1 microscope with Apotome, salient information was obtained from images of dopaminergic neurons using a structural feature extraction technique. A data set of 100 images of neurons was generated and a set of 17 features was used to describe their morphology. In order to identify differences between neurons, 2-dimensional and 3-dimensional image representations were analyzed. This paper compares the performance of three popular classification methods in bioimage classification (Support Vector Machines (SVMs), Back Propagation Neural Networks (BPNNs) and Multinomial Logistic Regression (MLR)), and the results show a significant difference between machine classification (with 97% accuracy) and human expert based classification (72% accuracy).
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent1976781 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherIEEE
dc.publisher.placeOnline
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencenameIJCNN 2009 - International Joint Conference on Neural Networks
dc.relation.ispartofconferencetitleProceedings of the International Joint Conference on Neural Networks
dc.relation.ispartofdatefrom2009-06-14
dc.relation.ispartofdateto2009-06-19
dc.relation.ispartoflocationAtlanta, Georgia, United States
dc.relation.ispartofpagefrom81
dc.relation.ispartofpageto88
dc.rights.retentionY
dc.subject.fieldofresearchNeurosciences not elsewhere classified
dc.subject.fieldofresearchcode320999
dc.titleAutomated classification of dopaminergic neurons in the rodent brain
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
gro.date.issued2009
gro.hasfulltextFull Text
gro.griffith.authorMackay-Sim, Alan
gro.griffith.authorTuxworth, Gervase


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  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

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