Journal
PEERJ
Volume 4, Issue -, Pages -Publisher
PEERJ INC
DOI: 10.7717/peerj.2207
Keywords
Neuron; Dendritic spine; Ridgelet detection; Twin Support vector machine
Categories
Funding
- National Natural Science Foundation of China [61271231]
- NIH [RO1AG033004, R01CA175360]
- Natural Science Foundation of Jiangsu Province [BK20150983]
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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.
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