Automated classification of dopaminergic neurons in the rodent brain

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Title Automated classification of dopaminergic neurons in the rodent brain
Author Alavi, Azadeh; Cavanagh, Brenton; Tuxworth, Gervase; Meedeniya, Adrian Cuda Banda; Mackay-Sim, Alan; Blumenstein, Michael
Publication Title IJCNN 2009 Conference Proceedings
Editor Robert Kozma
Year Published 2009
Place of publication Online
Publisher IEEE
Abstract Accurate 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).
Peer Reviewed Yes
Published Yes
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Copyright Statement 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.
Conference name IJCNN 2009 - International Joint Conference on Neural Networks
Location Atlanta, Georgia, United States
Date From 2009-06-14
Date To 2009-06-19
Date Accessioned 2009-10-19
Language en_AU
Research Centre Eskitis Institute for Drug Discovery; Institute for Integrated and Intelligent Systems; Molecular Basis of Disease
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Neural, Evolutionary and Fuzzy Computation; Neurosciences; Pattern Recognition and Data Mining
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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