TY - JOUR A2 - Makarov, Valeri AU - Wang, shuhua AU - Chen, Mengmeng AU - Li, Yang AU - Zhang, yu - dong AU - Han, Liangxiu AU - Wu, Jane AU - Du，四单PY - 2015 DA - 2015/11/24 TI -树突棘使用条件对称小波分析检测和正则化神经网络形态Shared-Weight SP - 454076六世- 2015 AB -识别和检测神经元树突棘的图像高兴趣的神经和精神病的诊断和治疗disorders (e.g., Alzheimer’s disease, Parkinson’s diseases, and autism). In this paper, we have proposed a novel automatic approach using wavelet-based conditional symmetric analysis and regularized morphological shared-weight neural networks (RMSNN) for dendritic spine identification involving the following steps: backbone extraction, localization of dendritic spines, and classification. First, a new algorithm based on wavelet transform and conditional symmetric analysis has been developed to extract backbone and locate the dendrite boundary. Then, the RMSNN has been proposed to classify the spines into three predefined categories (mushroom, thin, and stubby). We have compared our proposed approach against the existing methods. The experimental result demonstrates that the proposed approach can accurately locate the dendrite and accurately classify the spines into three categories with the accuracy of 99.1% for “mushroom” spines, 97.6% for “stubby” spines, and 98.6% for “thin” spines. SN - 1748-670X UR - https://doi.org/10.1155/2015/454076 DO - 10.1155/2015/454076 JF - Computational and Mathematical Methods in Medicine PB - Hindawi Publishing Corporation KW - ER -