PURPOSE: To develop rapid image processing techniques for the objective analysis of corneal in vivo confocal micrographs. METHODS: Perpendicular central corneal volume scans from healthy volunteers were obtained via laser in vivo confocal microscopy. The layer in each volume scan that contained the nerve plexus was detected by applying software operators to analyze image features on the basis of their size, shape, and contrast. Dendritic immune cells were detected in the nerve image on the basis of cellular size, lack of elongation, and brightness relative to the nerves. Images that were 20 μm anterior to the best nerve layer images were used for the analysis of epithelial wing cells; wing cell detection was based on extended regional minima and a watershed transformation. RESULTS: The software successfully detected the best nerve layer images in 15 scans from 15 eyes. Manual and automatic analyses were 81.8% in agreement for dendritic immune cells (for 11 cells in a representative image) and 94.4% in agreement for wing cells (for 466 cells in the image). Within 10 seconds per scan, the software calculated the number, mean length, and mean density of immune cells; the number, mean size, and mean density of wing cells; and the number and mean length of nerves. Factors defining the shape and position of cells and nerves also were available. CONCLUSIONS: The software rapidly and accurately analyzed the in vivo confocal micrographs of the healthy central corneas, yielding quantitative results to describe the nerves, dendritic immune cells, and wing cells.
PURPOSE: To develop rapid image processing techniques for the objective analysis of corneal in vivo confocal micrographs. METHODS: Perpendicular central corneal volume scans from healthy volunteers were obtained via laser in vivo confocal microscopy. The layer in each volume scan that contained the nerve plexus was detected by applying software operators to analyze image features on the basis of their size, shape, and contrast. Dendritic immune cells were detected in the nerve image on the basis of cellular size, lack of elongation, and brightness relative to the nerves. Images that were 20 μm anterior to the best nerve layer images were used for the analysis of epithelial wing cells; wing cell detection was based on extended regional minima and a watershed transformation. RESULTS: The software successfully detected the best nerve layer images in 15 scans from 15 eyes. Manual and automatic analyses were 81.8% in agreement for dendritic immune cells (for 11 cells in a representative image) and 94.4% in agreement for wing cells (for 466 cells in the image). Within 10 seconds per scan, the software calculated the number, mean length, and mean density of immune cells; the number, mean size, and mean density of wing cells; and the number and mean length of nerves. Factors defining the shape and position of cells and nerves also were available. CONCLUSIONS: The software rapidly and accurately analyzed the in vivo confocal micrographs of the healthy central corneas, yielding quantitative results to describe the nerves, dendritic immune cells, and wing cells.
Authors: B S Kowtharapu; K Winter; C Marfurt; S Allgeier; B Köhler; M Hovakimyan; T Stahnke; A Wree; O Stachs; R F Guthoff Journal: Eye (Lond) Date: 2016-11-04 Impact factor: 3.775
Authors: Ahmad Kheirkhah; Rodrigo Muller; Janine Mikolajczak; Ai Ren; Ella Maria Kadas; Hanna Zimmermann; Harald Pruess; Friedemann Paul; Alexander U Brandt; Pedram Hamrah Journal: Invest Ophthalmol Vis Sci Date: 2015-09 Impact factor: 4.799
Authors: Xin Chen; Jim Graham; Mohammad A Dabbah; Ioannis N Petropoulos; Mitra Tavakoli; Rayaz A Malik Journal: IEEE Trans Biomed Eng Date: 2016-06-07 Impact factor: 4.538