title: Watershed deconvolution for cell segmentation.
abstract: Cell segmentation and/or localization is the first stage of a (semi)automatic tracking system. We addressed the cell localization problem in our previous work where we characterized a typical blood stem cell in a microscopic image as an approximately circular object with dark interior and bright boundary. We also addressed the modelling of adjacent and dividing cells in our previous work as a deconvolution method to model individual blood stem cell as well as adjacent and dividing blood stem cells where an optimization algorithm was combined with a template matching method to segment cell regions and locate the cell centers. Our previous cell deconvolution method is capable of modelling different cell types with changes in the model parameters. However in cases where either a complex parameterized shape is needed to model a specific cell type, or in place of cell center localization, an exact cell segmentation is needed, this method will not be effective. In this paper we propose a method to achieve cell boundary segmentation. Considering cell segmentation as an inverse problem, we assume that cell centers are located in advance. Then, the cell segmentation will be solved by finding cell regions for optimal representation of cell centers while a template matching method is effectively employed to localize cell centres.
authors.affiliation: Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada.
authors.forename: Nezamoddin N
authors.initials: NN
authors.lastname: Kachouie
authors.forename: Paul
authors.initials: P
authors.lastname: Fieguth
authors.forename: Eric
authors.initials: E
authors.lastname: Jervis
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002478:Cells, Cultured; D006412:Hematopoietic Stem Cells / Q000166:cytology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity