Abstract:
: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.
journal_name
PeerJjournal_title
PeerJauthors
Wang S,Chen M,Li Y,Shao Y,Zhang Y,Du S,Wu Jdoi
10.7717/peerj.2207subject
Has Abstractpub_date
2016-07-20 00:00:00pages
e2207issn
2167-8359pii
2207journal_volume
4pub_type
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