title: Segmentation of virus particle candidates in transmission electron microscopy images.
abstract: In this paper, we present an automatic segmentation method that detects virus particles of various shapes in transmission electron microscopy images. The method is based on a statistical analysis of local neighbourhoods of all the pixels in the image followed by an object width discrimination and finally, for elongated objects, a border refinement step. It requires only one input parameter, the approximate width of the virus particles searched for. The proposed method is evaluated on a large number of viruses. It successfully segments viruses regardless of shape, from polyhedral to highly pleomorphic.
authors.affiliation: Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University, Uppsala, Sweden. gustaf.kylberg@cb.uu.se
authors.forename: G
authors.initials: G
authors.lastname: Kylberg
authors.forename: M
authors.initials: M
authors.lastname: Uppström
authors.forename: K-O
authors.initials: KO
authors.lastname: Hedlund
authors.forename: G
authors.initials: G
authors.lastname: Borgefors
authors.forename: I-M
authors.initials: IM
authors.lastname: Sintorn
mesh_terms: D005243:Feces / Q000821:virology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007091:Image Processing, Computer-Assisted; D046529:Microscopy, Electron, Transmission / Q000379:methods*; D009055:Mouth / Q000821:virology; D009296:Nasal Cavity / Q000821:virology; D010363:Pattern Recognition, Automated; D014771:Virion / Q000648:ultrastructure*; D014773:Virology / Q000379:methods*; D014777:Virus Diseases / Q000821:virology; D014780:Viruses / Q000145:classification* / Q000302:isolation & purification / Q000648:ultrastructure*