Show simple item record

dc.contributor.authorAlavi, Azadehen_US
dc.contributor.authorCavanagh, Brentonen_US
dc.contributor.authorTuxworth, Gervaseen_US
dc.contributor.authorMeedeniya, Adrianen_US
dc.contributor.authorMackay-Sim, Alanen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.editorRobert Kozmaen_US
dc.date.accessioned2017-05-03T16:59:18Z
dc.date.available2017-05-03T16:59:18Z
dc.date.issued2009en_US
dc.date.modified2011-10-11T07:14:12Z
dc.identifier.refurihttp://www.ijcnn2009.com/en_AU
dc.identifier.doi10.1109/IJCNN.2009.5178740en_AU
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).en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent1976781 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherIEEEen_US
dc.publisher.placeOnlineen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencenameIJCNN 2009 - International Joint Conference on Neural Networksen_US
dc.relation.ispartofconferencetitleIJCNN 2009 Conference Proceedingsen_US
dc.relation.ispartofdatefrom2009-06-14en_US
dc.relation.ispartofdateto2009-06-19en_US
dc.relation.ispartoflocationAtlanta, Georgia, United Statesen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchPattern Recognition and Data Miningen_US
dc.subject.fieldofresearchNeural, Evolutionary and Fuzzy Computationen_US
dc.subject.fieldofresearchNeurosciences not elsewhere classifieden_US
dc.subject.fieldofresearchcode080109en_US
dc.subject.fieldofresearchcode080108en_US
dc.subject.fieldofresearchcode110999en_US
dc.titleAutomated classification of dopaminergic neurons in the rodent brainen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 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.en_AU
gro.date.issued2009
gro.hasfulltextFull Text


Files in this item

This item appears in the following Collection(s)

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

Show simple item record