title: Detection of Dendritic Spines Using Wavelet-Based Conditional Symmetric Analysis and Regularized Morphological Shared-Weight Neural Networks.
abstract: Identification and detection of dendritic spines in neuron images are of high interest in diagnosis and treatment of neurological and psychiatric 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.
authors.affiliation: Department of Electronic Engineering, Nanjing University, Nanjing 210024, China ; School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China.
authors.forename: Shuihua
authors.initials: S
authors.lastname: Wang
authors.affiliation: State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China ; Department of Neurology, Lurie Cancer Center, Center for Genetic Medicine, Northwestern University School of Medicine, Chicago, IL 60611, USA ; University of Chinese Academy of Sciences, Beijing 100101, China.
authors.forename: Mengmeng
authors.initials: M
authors.lastname: Chen
authors.affiliation: Department of Electronic Engineering, Nanjing University, Nanjing 210024, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Li
authors.affiliation: School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China ; Translational Imaging Division, Columbia University, New York, NY 10032, USA.
authors.forename: Yudong
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester M1 5GD, UK.
authors.forename: Liangxiu
authors.initials: L
authors.lastname: Han
authors.affiliation: State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China ; Department of Neurology, Lurie Cancer Center, Center for Genetic Medicine, Northwestern University School of Medicine, Chicago, IL 60611, USA.
authors.forename: Jane
authors.initials: J
authors.lastname: Wu
authors.affiliation: Department of Electronic Engineering, Nanjing University, Nanjing 210024, China.
authors.forename: Sidan
authors.initials: S
authors.lastname: Du
mesh_terms: D000465:Algorithms; D000818:Animals; D002478:Cells, Cultured; D019295:Computational Biology; D049229:Dendritic Spines / Q000145:classification / Q000648:ultrastructure*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods / Q000706:statistics & numerical data; D021621:Imaging, Three-Dimensional / Q000379:methods / Q000706:statistics & numerical data; D018613:Microscopy, Confocal; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods / Q000706:statistics & numerical data; D051381:Rats; D058067:Wavelet Analysis