A new expert system based on fuzzy logic and image processing algorithms for early glaucoma diagnosis. (February 2018) - Soltani, A.; Battikh, T.; Jabri, I.; Lakhoua, N.
Highlights: A novel system based on fuzzy logic for early has hereupon been developped for early glaucoma diagnosis. The present method not only analyses the usual instrument-based ((CDR, ISNT rule, eyes' asymmetry), but also examines for the first time the risk factors in glaucoma detection such as age, family ancestry and race. The proposed system has achieved an accuracy of 96.15%, a sensitivity of 97.8% and a specificity of 94.8% reaching an improvement of 1–9% over earlier methods. The results were analyzed and compares with the most recent similar works. Abstract: Decision-making systems based on images have increasingly become essential nowadays mostly in the medical field. Indeed, the image has become one of the most fundamental tools for both clinical research and sicknesses' diagnosis. In this context, we treat glaucoma disease which can affect the optic nerve head (ONH), thus causing its destruction and leading to an irreversible vision loss. This paper presents a new glaucoma Fuzzy Expert System for early glaucoma diagnosis. Original ONH images are first pre-treated using appropriate filters to remove the noise. Canny detector algorithm is then used to detect the contours. Main parameters are then extracted, after having identified elliptical forms of both optic disc and excavation. This operation is performed by using Randomized Hough Transform. Finally, a classification algorithm, based on fuzzy logic approaches, is proposed to determine patients' conditions. Our system is advantageous as far as it takes into consideration both instrumental parameters and risk factors (age, race, family history…) which make an important contribution to the valuable identification of cases suspected to have glaucoma. The proposed system is tested on a real dataset of ophthalmologic images of both normal and glaucomatous cases. Compared with other existing systems, the experimental results show the superiority of the proposed methods. The percentage of good predictions is more than 96%, reaching an improvement of 1–9% over earlier methods.