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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 454076, 12 pages
Research Article

Detection of Dendritic Spines Using Wavelet-Based Conditional Symmetric Analysis and Regularized Morphological Shared-Weight Neural Networks

1Department of Electronic Engineering, Nanjing University, Nanjing 210024, China
2School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China
3State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
4Department of Neurology, Lurie Cancer Center, Center for Genetic Medicine, Northwestern University School of Medicine, Chicago, IL 60611, USA
5University of Chinese Academy of Sciences, Beijing 100101, China
6Translational Imaging Division, Columbia University, New York, NY 10032, USA
7School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester M1 5GD, UK

Received 17 June 2015; Revised 2 September 2015; Accepted 27 September 2015

Academic Editor: Valeri Makarov

Copyright © 2015 Shuihua Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Identification and detection of dendritic spines in neuron images are of high interest in diagnosis and treatment of neurological and psychiatric disorders (e.g., Alzheimer’s disease, Parkinson’s diseases, and autism). In this paper, we have proposed a novel automatic approach using wavelet-based conditional symmetric analysis and regularized morphological shared-weight neural networks (RMSNN) for dendritic spine identification involving the following steps: backbone extraction, localization of dendritic spines, and classification. First, a new algorithm based on wavelet transform and conditional symmetric analysis has been developed to extract backbone and locate the dendrite boundary. Then, the RMSNN has been proposed to classify the spines into three predefined categories (mushroom, thin, and stubby). We have compared our proposed approach against the existing methods. The experimental result demonstrates that the proposed approach can accurately locate the dendrite and accurately classify the spines into three categories with the accuracy of 99.1% for “mushroom” spines, 97.6% for “stubby” spines, and 98.6% for “thin” spines.