Abstract
PURPOSE: We describe a novel fully automatic method capable of tracing the subbasal plexus nerves from human corneal confocal images. METHODS: Following an increasing interest in the automatic analysis of corneal nerves, a few approaches have been proposed. These, however, cannot cope with large images, such as mosaics, in due time. The rationale of the proposed method is to minimize required computing time while still providing accurate results. Our method consists of two sequential steps - a thresholding step followed by a supervised classification. For the classification we use a support vector machines (SVM) approach. Initially, a large set of features is computed, which is later reduced using a backward-elimination based on segmentation accuracy. To validate the obtained tracings, we evaluated the tracing accuracy and reliability of extracted clinical parameters (corneal nerves density and tortuosity). RESULTS: The proposed algorithm proved capable to correctly trace 0.89 ± 0.07 of the corneal nerves. The obtained performance level was comparable to a second human grader. Furthermore, the proposed approach compares favorably to other methods. For both evaluated clinical parameters the proposed approach performed well. An execution time of 0.61 ± 0.07 seconds per image was achieved. The proposed algorithm was applied successfully to mosaic images, with run times of the order of tens of seconds. CONCLUSIONS: The achieved quality and processing time of the proposed method appear adequate for the application of this technique to clinical practice. TRANSLATIONAL RELEVANCE: The automatic tracing of corneal nerves is an important step for the quantitative analysis of corneal nerves in daily clinical practice. The proposed fast technique allows features, such as corneal nerve density and tortuosity, to be computed in a few seconds. The application of nerve tracing to mosaics covering a large area can be a key component in clinical studies aimed at investigating neuropathy influence in various ocular or systemic diseases.