TY - JOUR
T1 - Detection of dendritic spines using wavelet packet entropy and fuzzy support vector machine
AU - Wang, Shuihua
AU - Li, Yang
AU - Shao, Ying
AU - Cattani, Carlo
AU - Zhang, Yudong
AU - Du, Sidan
N1 - Publisher Copyright:
© 2017 Bentham Science Publishers.
PY - 2017
Y1 - 2017
N2 - 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%.
AB - 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%.
KW - Dendritic spines
KW - Discrete wavelet transform
KW - Fuzzy support vector machine
KW - Wavelet packet entropy
UR - http://www.scopus.com/inward/record.url?scp=85015669633&partnerID=8YFLogxK
U2 - 10.2174/1871527315666161111123638
DO - 10.2174/1871527315666161111123638
M3 - Review article
C2 - 27834129
AN - SCOPUS:85015669633
SN - 1871-5273
VL - 16
SP - 116
EP - 121
JO - CNS and Neurological Disorders - Drug Targets
JF - CNS and Neurological Disorders - Drug Targets
IS - 2
ER -