title: A novel method for dendritic spines detection based on directional morphological filter and shortest path.
abstract: Dendritic spines are tiny membranous protrusions from neuron's dendrites. They play a very important role in the nervous system. A number of mental diseases such as Alzheimer's disease and mental retardation are revealed to have close relations with spine morphologies or spine number changes. Spines have various shapes, and spine images are often not of good quality; hence it is very challenging to detect spines in neuron images. This paper presents a novel pipeline to detect dendritic spines in 2D maximum intensity projection (MIP) images and a new dendrite backbone extraction method is developed in the pipeline. The strategy for the backbone extraction approach is that it iteratively refines the extraction result based on directional morphological filtering and improved Hessian filtering until a satisfactory extraction result is obtained. A shortest path method is applied along a backbone to extract the boundary of the dendrites. Spines are then segmented from the dendrites outside the extracted boundary. Touching spines will be split using a marker-controlled watershed algorithm. We present the results of our algorithm on real images and compare our algorithm with two other spine detection methods. The results show that the proposed approach can detect dendrites and spines more accurately. Measurements and classification of spines are also made in this paper.
authors.affiliation: CSIRO Computational Informatics, Locked Bag 17, North Ryde, NSW 1670, Australia; School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT 2600, Australia. Electronic address: suran.tju@gmail.com.
authors.forename: Ran
authors.initials: R
authors.lastname: Su
authors.affiliation: CSIRO Computational Informatics, Locked Bag 17, North Ryde, NSW 1670, Australia. Electronic address: changming.sun@csiro.au.
authors.forename: Changming
authors.initials: C
authors.lastname: Sun
authors.affiliation: CSIRO Computational Informatics, Locked Bag 17, North Ryde, NSW 1670, Australia; School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT 2600, Australia. Electronic address: kone.zhang@gmail.com.
authors.forename: Chao
authors.initials: C
authors.lastname: Zhang
authors.affiliation: Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan. Electronic address: tdpham@u-aizu.ac.jp.
authors.forename: Tuan D
authors.initials: TD
authors.lastname: Pham
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D049229:Dendritic Spines / Q000648:ultrastructure*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Backbone extraction; Boundary detection; Dendrite; Dendritic spines; Shortest path