Detection of Dendritic Spines Using Wavelet Packet Entropy and Fuzzy Support Vector Machine.
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%.
Jiangsu Key Laboratory of 3d Printing Equipment And Manufacturing, Nanjing, Jiangsu 210042, China.
Shuihua
S
Wang
Yang
Y
Li
Ying
Y
Shao
Carlo
C
Cattani
Yudong
Y
Zhang
Department of Electronic Engineering, Nanjing University, Nanjing 210024, China.
Sidan
S
Du
D000818:Animals; D002478:Cells, Cultured; D049229:Dendritic Spines; D019277:Entropy; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D018613:Microscopy, Confocal; D015233:Models, Statistical; D060388:Support Vector Machine; D058067:Wavelet Analysis
Dendritic spines; discrete wavelet transform; fuzzy support vector machine; wavelet packet entropy