Detection of Dendritic Spines Using Wavelet Packet Entropy and Fuzzy Support Vector Machine

Author(s): Shuihua Wang, Yang Li, Ying Shao, Carlo Cattani, Yudong Zhang, Sidan Du

Journal Name: CNS & Neurological Disorders - Drug Targets
Formerly Current Drug Targets - CNS & Neurological Disorders

Volume 16 , Issue 2 , 2017

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Graphical Abstract:


The morphology of dendritic spines is highly correlated with the neuron function. Therefore, it is of positive influence for the research of the dendritic spines. However, it is tried to manually label the spine types for statistical analysis. In this work, we proposed an approach based on the combination of wavelet contour analysis for the backbone detection, wavelet packet entropy, and fuzzy support vector machine for the spine classification. The experiments show that this approach is promising. The average detection accuracy of “MushRoom” achieves 97.3%, “Stubby” achieves 94.6%, and “Thin” achieves 97.2%.

Keywords: Dendritic spines, discrete wavelet transform, fuzzy support vector machine, wavelet packet entropy.

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Article Details

Year: 2017
Published on: 11 November, 2016
Page: [116 - 121]
Pages: 6
DOI: 10.2174/1871527315666161111123638
Price: $65

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