Automated classification of dopaminergic neurons in the rodent brain

Alavi, Azadeh, Cavanagh, Brenton, Tuxworth, Gervase, Meedeniya, Adrian, Mackay-Sim, Alan and Blumenstein, Michael (2009). Automated classification of dopaminergic neurons in the rodent brain. In: Proceedings of International Joint Conference on Neural Networks. International Joint Conference on Neural Networks (IJCNN 2009), Atlanta, United States, (81-88). 14-19 June 2009. doi:10.1109/IJCNN.2009.5178740


Author Alavi, Azadeh
Cavanagh, Brenton
Tuxworth, Gervase
Meedeniya, Adrian
Mackay-Sim, Alan
Blumenstein, Michael
Title of paper Automated classification of dopaminergic neurons in the rodent brain
Conference name International Joint Conference on Neural Networks (IJCNN 2009)
Conference location Atlanta, United States
Conference dates 14-19 June 2009
Proceedings title Proceedings of International Joint Conference on Neural Networks
Journal name International Joint Conference on Neural Networks. Proceedings
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2009
Sub-type Fully published paper
DOI 10.1109/IJCNN.2009.5178740
Open Access Status Not yet assessed
ISBN 9781424435487
9781424435531
ISSN 1098-7576
Start page 81
End page 88
Total pages 8
Language eng
Abstract/Summary 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).
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Conference Paper
Sub-type: Fully published paper
Collection: School of Information Technology and Electrical Engineering Publications
 
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Created: Fri, 10 Jan 2014, 01:36:36 EST by Azadeh Alavi on behalf of School of Information Technol and Elec Engineering