RESUMO
PURPOSE: Creating wide-field montages of the human corneal subbasal nerve plexus using laser scanning in vivo confocal microscopy (IVCM) requires considerable expertise and remains highly labor intensive. A typical montage contains several hundred images to be quality checked and manually arranged. The purpose of this study was to develop and validate software for off-line montaging of IVCM images of the living human cornea. METHODS: The software was developed and tested using four large data sets of IVCM images from normal human corneas. Two of the data sets were used for calibration purposes, the remaining images served as a validation set. Techniques utilized included image binarization, clustering, key-point generation, and feature-based stitching. A range of tests involving computer processing and visual inspection were applied to audit and compare the automated montages with manually constructed montages. RESULTS: The original IVCM images (N = 2565) from four corneas were processed into image groups, reducing the number of effective images by 68% to 86%. Each data set contained a large primary grouping. A clustering strategy was used to reduce the total potential workload by 57%. Both programmatic and visual inspection confirmed the method was robust to errors, with a specificity of 100% (i.e., no falsely matched images). The time taken to complete the montage varied from 1.5 to 3 hours. METHODS: Computer-driven image stitching is a useful, effective, and time-saving tool for studies involving IVCM corneal nerve imaging. Further research will extend and optimize these
