title: Corneal endothelium cell field analysis by means of interacting Bayesian shape models.
abstract: A new method is proposed for the automatic detection and analysis of cell field contours in images of corneal endothelium. The algorithm is based on a set of single-cell contour models (a cell field), individually described statistically in term of shape a-priori information and a-posteriori image representation. Each cell can be individually identified (Maximum A Posteriori estimation) on the available image given a starting point and an appropriate optimization algorithm. Simulated Annealing has been adopted as the optimization algorithm to overcome the presence of several local minima in the resulting criterion function. When a cell field is considered, interaction between cell models can be used to introduce further information and improve the overall model identification. A statistical description of the cell field model is given by considering interaction between cell models. Preliminary results show that the extension from single cell models to field models improves the cell contours recognition. The developed theoretical framework is extremely flexible and can be easily adapted to different prior distributions or even to different object detection applications involving shape prior information.
authors.affiliation: Department of Information Engineering, University of Padova and M2 Scientific Computing, Italy. foracchia@m2scientific.it
authors.forename: Marco
authors.initials: M
authors.lastname: Foracchia
authors.forename: Alfredo
authors.initials: A
authors.lastname: Ruggeri
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D042783:Endothelial Cells / Q000166:cytology*; D004728:Endothelium, Corneal / Q000166:cytology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity