title: Influence of applied corneal endothelium image segmentation techniques on the clinical parameters.
abstract: The corneal endothelium state is verified on the basis of an in vivo specular microscope image from which the shape and density of cells are exploited for data description. Due to the relatively low image quality resulting from a high magnification of the living, non-stained tissue, both manual and automatic analysis of the data is a challenging task. Although, many automatic or semi-automatic solutions have already been introduced, all of them are prone to inaccuracy. This work presents a comparison of four methods (fully-automated or semi-automated) for endothelial cell segmentation, all of which represent a different approach to cell segmentation; fast robust stochastic watershed (FRSW), KH method, active contours solution (SNAKE), and TOPCON ImageNET. Moreover, an improvement framework is introduced which aims to unify precise cell border location in images pre-processed with differing techniques. Finally, the influence of the selected methods on clinical parameters is examined, both with and without the improvement framework application. The experiments revealed that although the image segmentation approaches differ, the measures calculated for clinical parameters are in high accordance when CV (coefficient of variation), and CVSL (coefficient of variation of cell sides length) are considered. Higher variation was noticed for the H (hexagonality) metric. Utilisation of the improvement framework assured better repeatability of precise endothelial cell border location between the methods while diminishing the dispersion of clinical parameter values calculated for such images. Finally, it was proven statistically that the image processing method applied for endothelial cell analysis does not influence the ability to differentiate between the images using medical parameters.
authors.affiliation: AGH University of Science and Technology, Department of Geoinformatics and Applied Computer Science, Cracow, Poland.
authors.forename: Adam
authors.initials: A
authors.lastname: Piorkowski
authors.affiliation: Silesian University of Technology, Institute of Informatics, Gliwice, Poland. Electronic address: Karolina.Nurzynska@polsl.pl.
authors.forename: Karolina
authors.initials: K
authors.lastname: Nurzynska
authors.affiliation: Pomeranian Medical University, Department of Ophthalmology, Szczecin, Poland; Sygehus Sønderjylland, Department of Ophthalmology, Sønderborg, Denmark.
authors.forename: Jolanta
authors.initials: J
authors.lastname: Gronkowska-Serafin
authors.affiliation: Uppsala University, Centre for Image Analysis, Uppsala, Sweden.
authors.forename: Bettina
authors.initials: B
authors.lastname: Selig
authors.affiliation: Bialystok University of Technology, Faculty of Computer Science, Bialystok, Poland.
authors.forename: Cezary
authors.initials: C
authors.lastname: Boldak
authors.affiliation: Bialystok University of Technology, Faculty of Computer Science, Bialystok, Poland.
authors.forename: Daniel
authors.initials: D
authors.lastname: Reska
mesh_terms: D000465:Algorithms; D048430:Cell Shape; D004728:Endothelium, Corneal / Q000166:cytology* / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated; D013269:Stochastic Processes
keywords: Image processing; Non-contact specular microscope; Segmentation; The corneal endothelium cells