title: Global gray-level thresholding based on object size.
abstract: In this article, we propose a fast and robust global gray-level thresholding method based on object size, where the selection of threshold level is based on recall and maximum precision with regard to objects within a given size interval. The method relies on the component tree representation, which can be computed in quasi-linear time. Feature-based segmentation is especially suitable for biomedical microscopy applications where objects often vary in number, but have limited variation in size. We show that for real images of cell nuclei and synthetic data sets mimicking fluorescent spots the proposed method is more robust than all standard global thresholding methods available for microscopy applications in ImageJ and CellProfiler. The proposed method, provided as ImageJ and CellProfiler plugins, is simple to use and the only required input is an interval of the expected object sizes. © 2016 International Society for Advancement of Cytometry.
authors.affiliation: Centre for Image Analysis and SciLifeLab, Uppsala University, Uppsala, Sweden.
authors.forename: Petter
authors.initials: P
authors.lastname: Ranefall
authors.affiliation: Centre for Image Analysis and SciLifeLab, Uppsala University, Uppsala, Sweden.
authors.forename: Carolina
authors.initials: C
authors.lastname: Wählby
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008853:Microscopy / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods
keywords: algorithms; automated; microscopy; pattern recognition