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Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine

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posted on 2018-05-15, 14:47 authored by S. Wang, M. Chen, Y. Li, Y. Shao, Yudong Zhang, S. Du, J. Wu
Dendritic spines are described as neuronal protrusions. The morphology of dendritic spines and dendrites has a strong relationship to its function, as well as playing an important role in understanding brain function. Quantitative analysis of dendrites and dendritic spines is essential to an understanding of the formation and function of the nervous system. However, highly efficient tools for the quantitative analysis of dendrites and dendritic spines are currently undeveloped. In this paper we propose a novel three-step cascaded algorithm–RTSVM— which is composed of ridge detection as the curvature structure identifier for backbone extraction, boundary location based on differences in density, the Hu moment as features and Twin Support Vector Machine (TSVM) classifiers for spine classification. Our data demonstrates that this newly developed algorithm has performed better than other available techniques used to detect accuracy and false alarm rates. This algorithm will be used effectively in neuroscience research.

History

Citation

PeerJ, 2016, 4:e2207

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Informatics

Version

  • VoR (Version of Record)

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PeerJ

Publisher

PeerJ

eissn

2167-8359

Acceptance date

2016-06-12

Copyright date

2016

Available date

2018-05-15

Publisher version

https://peerj.com/articles/2207/

Language

en

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