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.
Graphical Abstract
CNS & Neurological Disorders - Drug Targets
Title:Detection of Dendritic Spines Using Wavelet Packet Entropy and Fuzzy Support Vector Machine
Volume: 16 Issue: 2
Author(s): Shuihua Wang, Yang Li, Ying Shao, Carlo Cattani, Yudong Zhang and Sidan Du
Affiliation:
Keywords: Dendritic spines, discrete wavelet transform, fuzzy support vector machine, wavelet packet entropy.
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%.
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Cite this article as:
Wang Shuihua, Li Yang, Shao Ying, Cattani Carlo, Zhang Yudong and Du Sidan, Detection of Dendritic Spines Using Wavelet Packet Entropy and Fuzzy Support Vector Machine, CNS & Neurological Disorders - Drug Targets 2017; 16 (2) . https://dx.doi.org/10.2174/1871527315666161111123638
DOI https://dx.doi.org/10.2174/1871527315666161111123638 |
Print ISSN 1871-5273 |
Publisher Name Bentham Science Publisher |
Online ISSN 1996-3181 |
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