Automatic recognition of corneal nerve structures in images from confocal microscopy

¹ Information Engineering, University of Padova, Padova, Italy
² Information Engineering, University of Padova, Via Gradenigo 6/a, Padova, 35131, Italy

^* To whom correspondence should be addressed. E-mail: alfredo.ruggeri{at}unipd.it.

	Abstract

PURPOSE. We address the problem of automatically tracing corneal nerves in confocal microscopy images. METHODS. Images were acquired using the ConfoScan4 confocal microscope (Nidek Technologies, Italy). They were normalized and enhanced as regards luminosity and contrast. Nerves were recognized by applying a novel tracing algorithm, which includes Gabor filtering to enhance nerve visibility and post-processing procedures to remove false recognitions and to link sparse segments into continuous structures. A prototype of the algorithm was implemented in the Matlab© language and run on a personal computer. RESULTS. A retrospective evaluation of the automatic procedure was performed on a data set containing 90 images, from normal and non normal subjects. The percent of correctly recognized nerves length with respect to total manually traced length of visible nerves was on average 80.4% in normal subjects and 83.8% on non normal subjects; the rate of false nerve length recognitions (with respect to the total automatically traced length) was on average 6.5% in normal subjects and 9.1 % in non normal subjects. Correlation coefficients between manual and automatic lengths on the same image were 0.94, 0.95, and 0.86 in all, normal, and non normal subjects, respectively. A further evaluation was performed on an independent set of 80 normal subject images, resulting in a correlation coefficient of 0.89 between manual and automatic nerve lengths. CONCLUSIONS. Automatic and manual length estimations on the same image were very well correlated, indicating that the automatic procedure is capable of correctly reproducing the differences in nerve length between different subjects.

Key Words: confocal microscopy, image analysis, morphometry

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