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

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%.

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