Abstract
Glaucoma is a chronic disease, and if not diagnosed at the early stage can lead to permanent blindness. For the detection of glaucoma, ophthalmologists use few medical techniques like Heidelberg Retinal Tomography (HRT) and Optical Coherence Tomography (OCT) for detecting glaucoma. These techniques are costly and time-consuming. Thus, automatic analysis of retina images is gaining more attention that can provide accurate results that are delivered faster than the manual process can achieve. Glaucoma increases the cup-to-disc ratio (CDR) and decreases the rim-to-disc ratio (RDR), affecting the peripheral vision loss. This work recommends a new denoising technique followed by active contour model calculation that depends on a texture-based procedure to diagnose the glaucoma on the CDR and RDR evaluation. The robustness of the recommended approach is evaluated on a most challenging real-time retinal database named as VISAKHA database. The retinal images are collected from the Visakha Eye Hospital, Visakhapatnam, AP, India. The suggested method is capable of detecting the glaucoma almost 94–96% successfully on the real-time dataset.
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Dash, S., Satish Rama Chowdary, P., Gopala Raju, C.V., Umamaheshwar, Y., Siva Charan, K.J.N. (2022). Optic Disc Segmentation Based on Active Contour Model for Detection and Evaluation of Glaucoma on a Real-Time Challenging Dataset. In: Satapathy, S.C., Peer, P., Tang, J., Bhateja, V., Ghosh, A. (eds) Intelligent Data Engineering and Analytics. Smart Innovation, Systems and Technologies, vol 266. Springer, Singapore. https://doi.org/10.1007/978-981-16-6624-7_37
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DOI: https://doi.org/10.1007/978-981-16-6624-7_37
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