| Literature DB >> 27834129 |
Shuihua Wang1, Yang Li, Ying Shao, Carlo Cattani, Yudong Zhang, Sidan Du2.
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%. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.Entities:
Keywords: Dendritic spines; discrete wavelet transform; fuzzy support vector machine; wavelet packet entropy
Mesh:
Year: 2017 PMID: 27834129 DOI: 10.2174/1871527315666161111123638
Source DB: PubMed Journal: CNS Neurol Disord Drug Targets ISSN: 1871-5273 Impact factor: 4.388