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

Journal: CNS & neurological disorders drug targets
PMID: 27834129

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%.

Authors

  • Shuihua Wang
    School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.
  • Yang Li
    Occupation of Chinese Center for Disease Control and Prevention, Beijing, China.
  • Ying Shao
  • Carlo Cattani
  • Yudong Zhang
    School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.
  • Sidan Du
    Department of Electronic Engineering, Nanjing University, Nanjing 210024, China.

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