ABSTRACT
PURPOSE: To assess the pigmentation distribution in the iridocorneal angle using an established algorithm with gonioscopically obtained images. METHODS: Manual and automatically modified Scheie's pigmentation grading (ie, 0/I=0, II=1 and III/IV=2) of trabecular meshwork was performed using an established algorithm on the 75 open-angle eyes of 75 subjects obtained by automated gonioscopy. All images were collected at the Matsue Red Cross Hospital in 2016. The differences in the pigmentation density were compared statistically between the automated and manual techniques and among the four sectors (ie, inferior, superior, temporal and nasal) and the four quadrants. RESULTS: There was substantial agreement between both grading methods (kappa value=0.70). There was no significant difference between the automated and manual grading in any sectors except for the superior (p=0.0004). The automated pigmentation grade was significantly (p<0.05) higher in the inferior sector (mean grade, 1.43) than in the others (mean grade, 0.48~0.76), and it was also significantly (p<0.05) higher in the inferior quadrant (mean grade, 3.56) than in the others (mean grade, 1.64~2.24). The findings were similar for manual grading. CONCLUSIONS: The entire distribution of the pigmentation in the anterior chamber angle was classified successfully using the algorithm, and the automated versus manual grading comparison showed good agreement. The automated pigmentation grading scores in the inferior sector and inferior quadrant were significantly higher than in the others as previously reported. Similar findings also were seen for manual grading.
Subject(s)
Algorithms , Anterior Chamber/diagnostic imaging , Glaucoma/diagnosis , Gonioscopy/instrumentation , Intraocular Pressure/physiology , Trabecular Meshwork/diagnostic imaging , Adult , Aged , Aged, 80 and over , Cross-Sectional Studies , Equipment Design , Female , Glaucoma/physiopathology , Humans , Male , Middle Aged , Tomography, Optical Coherence/methods , Young AdultABSTRACT
PURPOSE: To present stimuli with varied sizes, colors, and patterns over a large range of luminance. METHODS: The filter bar used in scotopic MP1 was replaced with a custom slide-in tray that introduces light from an external projector driven by an additional computer. MP1 software was modified to provide retinal tracking information to the computer driving the projector. Retinal tracking performance was evaluated by imaging the system input and the output simultaneously with a high-speed video system. Spatial resolution was measured with achromatic and chromatic grating/background combinations over scotopic and photopic ranges. RESULTS: The range of retinal illuminance achievable by the modification was up to 6.8 log photopic Trolands (phot-Td); however, in the current work, only a lower range over -4 to +3 log phot-Td was tested in human subjects. Optical magnification was optimized for low-vision testing with gratings from 4.5 to 0.2 cyc/deg. In normal subjects, spatial resolution driven by rods, short wavelength-sensitive (S-) cones, and long/middle wavelength-sensitive (L/M-) cones was obtained by the choice of adapting conditions and wavelengths of grating and background. Data from a patient with blue cone monochromacy was used to confirm mediation. CONCLUSIONS: The modified MP1 can be developed into an outcome measure for treatments in patients with severe retinal degeneration, very low vision, and abnormal eye movements such as those for whom treatment with optogenetics is planned, as well as for patients with cone disorders such as blue cone monochromacy for whom treatment with gene therapy is planned to improve L/M-cone function above a normal complement of rod and S-cone function.
Subject(s)
Color Vision Defects/therapy , Color Vision/physiology , Genetic Therapy/methods , Light , Optogenetics/methods , Retinal Cone Photoreceptor Cells/pathology , Retinal Degeneration/therapy , Adolescent , Adult , Aged , Color Vision Defects/diagnosis , Color Vision Defects/etiology , Contrast Sensitivity/physiology , Eye Movements/physiology , Feasibility Studies , Female , Follow-Up Studies , Humans , Male , Middle Aged , Retinal Degeneration/complications , Retinal Degeneration/diagnosis , Severity of Illness Index , Visual Perception/physiology , Young AdultABSTRACT
A confocal microscope can produce gray-scale images of the different layers of the cornea. We have addressed the problem of classifying these images, i.e. recognizing the layer displayed, using the shape of the cells contained, which is uniquely related to each specific layer. A first method was designed, based first on the binarization of the image and then on the description of the cell shape by means of Hu variables (central moments). An artificial neural network was used to classify each image according to the values assumed by these variables. A Matlab prototype of the classification system was developed, considering images of three corneal layers (Bowman membrane, stroma, endothelium) in normal subjects. The system was tested on 46 images, and good results were obtained. To avoid the critical step of binarization, an alternative cell shape description was investigated, based on Zernike moments, and a new network was developed and trained. The results achieved were better than those obtained with the previous technique, and also, no binarization was necessary.
