Abstract
Glaucoma is one of the major abnormalities in the eye which leads to permanent vision loss over time if left untreated. Prolonged high level of intraocular pressure (IOP) causes optic nerve damage leading to glaucoma. Glaucoma can be detected by analyzing the characteristics of the optic nerve head (ONH) and retinal nerve fiber layer (RNFL). The quantitative analysis of the ocular details obtained by the imaging techniques can help in disease management. The algorithm used here automatically extracts clinical features such as anterior chamber width, iris endpoint width, chamber height, lens vault, angle opening distance, and trabecular iris angle from AS-OCT images to find the condition of eye, namely, the presence or absence of glaucoma.
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Acknowledgements
The authors wish to thank Dr. Ronnie Jacob George, Director Research, Vision Research Foundation, Chennai for his valuable suggestions.
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Priyanka, P., Norris Juliet, V., Shenbaga Devi, S. (2019). Detection of Glaucoma Using Anterior Segment Optical Coherence Tomography Images. In: Peter, J., Fernandes, S., Eduardo Thomaz, C., Viriri, S. (eds) Computer Aided Intervention and Diagnostics in Clinical and Medical Images. Lecture Notes in Computational Vision and Biomechanics, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-04061-1_30
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DOI: https://doi.org/10.1007/978-3-030-04061-1_30
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