Abstract
Glaucoma is an irreversible disease of the eyes that prevails over the optic nerve head of the retinal image due to upsurge in intra-ocular pressure. Due to the asymptomatic and slow progressive nature of glaucoma, many people are unaware of this visual upheaval until it stretches an intricate stage. As of now prevention is the only mean to avoid vision loss and hence periodical screening of retinal image is essential. Creating awareness among the people and reasonably priced diagnosing techniques are the need of the hour. The aim of this analysis is to design an algorithmic method to identify primary open-angle glaucoma in a cost effective way. The vertical and horizontal optic cup-to-disc ratio, neuroretinal rim area and ISNT rule analysis are the prime factors of detecting the glaucomatous eye. Optic disc and cup feature extraction is prerequisite to analyze, interpret and predict the abnormality. The proposed method is a multivariate approach that use K-means clustering concept in segregating the optic disc and cup from retina, measures the cup-to-disc ratio vertically and horizontally, estimate neuro retinal rim area and ISNT rule analysis automatically. It is tested on imaging dataset Bin Rushed and Magrabi Eye center file of Retinal Fundus Images for Glaucoma Analysis, to qualify the images as either normal or glaucomatous. The results have been compared with the ophthalmologist’s prediction. A graphical user interface has been designed and implemented using MATLAB.
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Antony Ammal, M., Gladis, D., Shaik, A. (2021). Metric Measures of Optic Nerve Head in Screening Glaucoma with Machine Learning. In: Peng, SL., Hao, RX., Pal, S. (eds) Proceedings of First International Conference on Mathematical Modeling and Computational Science. Advances in Intelligent Systems and Computing, vol 1292. Springer, Singapore. https://doi.org/10.1007/978-981-33-4389-4_54
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DOI: https://doi.org/10.1007/978-981-33-4389-4_54
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