Peer Reviewed

1

DOI

10.17077/omia.1051

Conference Location

Athens, Greece

Publication Date

October 2016

Abstract

In-vivo specular and confocal microscopy provide information on the corneal endothelium health state. The reliable estimation of the clinical parameters requires the accurate detection of cell contours. We propose a method for the automatic segmentation of cell contour. The centers of the cells are detected by convolving the original image with Laplacian of Gaussian kernels, whose scales are set according to the cell size preliminary estimated through a frequency analysis. A structure made by connected vertices is derived from the centers, and it is fine-tuned by combining information about the typical regularity of endothelial cells shape with the pixels intensity of the actual image. Ground truth values for the clinical parameters were obtained from manually drawn cell contours. An accurate automatic estimation is achieved on 30 images: for each clinical parameter, the mean difference between its manual estimation and the automated one is always less than 7%.

Rights

Copyright © 2016 the authors

Included in

Ophthalmology Commons

Share

COinS
 
Oct 21st, 12:00 AM Oct 21st, 12:00 AM

Automated Morphometric Analysis of in-vivo Human Corneal Endothelium

Athens, Greece

In-vivo specular and confocal microscopy provide information on the corneal endothelium health state. The reliable estimation of the clinical parameters requires the accurate detection of cell contours. We propose a method for the automatic segmentation of cell contour. The centers of the cells are detected by convolving the original image with Laplacian of Gaussian kernels, whose scales are set according to the cell size preliminary estimated through a frequency analysis. A structure made by connected vertices is derived from the centers, and it is fine-tuned by combining information about the typical regularity of endothelial cells shape with the pixels intensity of the actual image. Ground truth values for the clinical parameters were obtained from manually drawn cell contours. An accurate automatic estimation is achieved on 30 images: for each clinical parameter, the mean difference between its manual estimation and the automated one is always less than 7%.