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

Shuihua Wang; Yang Li; Ying Shao; Carlo Cattani; Yudong Zhang; Sidan Du

doi:10.2174/1871527315666161111123638

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

CNS Neurol Disord Drug Targets. 2017;16(2):116-121. doi: 10.2174/1871527315666161111123638.

Authors

Shuihua Wang¹, Yang Li, Ying Shao, Carlo Cattani, Yudong Zhang, Sidan Du²

Affiliations

¹ Jiangsu Key Laboratory of 3d Printing Equipment And Manufacturing, Nanjing, Jiangsu 210042, China.
² Department of Electronic Engineering, Nanjing University, Nanjing 210024, China.

PMID: 27834129
DOI: 10.2174/1871527315666161111123638

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.

MeSH terms

Animals
Cells, Cultured
Dendritic Spines*
Entropy
Image Processing, Computer-Assisted / methods*
Microscopy, Confocal
Models, Statistical
Support Vector Machine*
Wavelet Analysis*