Detection of Dendritic Spines Using Wavelet Packet Entropy and Fuzzy Support Vector Machine

被引：61

作者：

Wang, Shuihua ^{[1
,2
]}

Li, Yang ^{[1
]}

Shao, Ying ^{[1
]}

Cattani, Carlo ^{[3
]}

Zhang, Yudong ^{[1
]}

Du, Sidan ^{[1
]}

机构：

[1] Nanjing Univ, Dept Elect Engn, Nanjing 210024, Jiangsu, Peoples R China

[2] Jiangsu Key Lab 3d Printing Equipment & Mfg, Nanjing 210042, Jiangsu, Peoples R China

[3] Univ Tuscia, Engn Sch, I-01100 Viterbo, Lazio, Italy

来源：

CNS & NEUROLOGICAL DISORDERS-DRUG TARGETS | 2017年 / 16卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Dendritic spines; discrete wavelet transform; fuzzy support vector machine; wavelet packet entropy; ALZHEIMERS-DISEASE; MICROSCOPY; TRANSFORM; IMAGES; FILTER; DISORDER;

D O I：

10.2174/1871527315666161111123638

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

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%.

引用

页码：116 / 121

页数：6

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