A Computer Vision Approach Based on the Retinal Nerve Fiber Thickness Analysis to Estimate the Risk of Suffering Glaucoma

M. Chazi-Solis; C. Cajamarca-Bueno; E. Pinos-Velez; V. Robles-Bykbaev; Carlos Luis Chacon

doi:10.1109/CONIITI.2018.8587082

A Computer Vision Approach Based on the Retinal Nerve Fiber Thickness Analysis to Estimate the Risk of Suffering Glaucoma

M. Chazi-Solis, C. Cajamarca-Bueno, E. Pinos-Velez, V. Robles-Bykbaev, Carlos Luis Chacon

Research output: Contribution to conference › Paper

Abstract

© 2018 IEEE. According to estimations of the World Health Organization (WHO), approximately 4.5 million persons will become blind due to the primary Glaucoma. For these reasons, in this paper, we present a simple approach to estimate the risk of suffering from glaucoma that presents a patient. Our proposal relies on the automatic analysis of Optical Coherence Tomography (OCT) images through the calculus of nerve fiber and ganglionary vessels areas. To this aim, our system separates the two areas mentioned before through computer vision techniques and the Newton Interpolation Method. Once the two areas are separated, the system determines the size of each of them. This proposal was validated through an experiment carried out with OCT 30 images (per eye). The results are encouraging, and the suggestions provided by the system match with medical diagnosis.

Original language	English (US)
DOIs	https://doi.org/10.1109/CONIITI.2018.8587082
State	Published - 21 Dec 2018
Event	2018 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2018 - Proceedings - Duration: 21 Dec 2018 → …

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Conference	2018 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2018 - Proceedings
Period	21/12/18 → …

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10.1109/CONIITI.2018.8587082

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Chazi-Solis, M., Cajamarca-Bueno, C., Pinos-Velez, E., Robles-Bykbaev, V., & Chacon, C. L. (2018). A Computer Vision Approach Based on the Retinal Nerve Fiber Thickness Analysis to Estimate the Risk of Suffering Glaucoma. Paper presented at 2018 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2018 - Proceedings. https://doi.org/10.1109/CONIITI.2018.8587082

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abstract = "{\textcopyright} 2018 IEEE. According to estimations of the World Health Organization (WHO), approximately 4.5 million persons will become blind due to the primary Glaucoma. For these reasons, in this paper, we present a simple approach to estimate the risk of suffering from glaucoma that presents a patient. Our proposal relies on the automatic analysis of Optical Coherence Tomography (OCT) images through the calculus of nerve fiber and ganglionary vessels areas. To this aim, our system separates the two areas mentioned before through computer vision techniques and the Newton Interpolation Method. Once the two areas are separated, the system determines the size of each of them. This proposal was validated through an experiment carried out with OCT 30 images (per eye). The results are encouraging, and the suggestions provided by the system match with medical diagnosis.",

author = "M. Chazi-Solis and C. Cajamarca-Bueno and E. Pinos-Velez and V. Robles-Bykbaev and Chacon, {Carlos Luis}",

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doi = "10.1109/CONIITI.2018.8587082",

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Chazi-Solis, M, Cajamarca-Bueno, C, Pinos-Velez, E , Robles-Bykbaev, V & Chacon, CL 2018, 'A Computer Vision Approach Based on the Retinal Nerve Fiber Thickness Analysis to Estimate the Risk of Suffering Glaucoma', Paper presented at 2018 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2018 - Proceedings, 21/12/18. https://doi.org/10.1109/CONIITI.2018.8587082

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AB - © 2018 IEEE. According to estimations of the World Health Organization (WHO), approximately 4.5 million persons will become blind due to the primary Glaucoma. For these reasons, in this paper, we present a simple approach to estimate the risk of suffering from glaucoma that presents a patient. Our proposal relies on the automatic analysis of Optical Coherence Tomography (OCT) images through the calculus of nerve fiber and ganglionary vessels areas. To this aim, our system separates the two areas mentioned before through computer vision techniques and the Newton Interpolation Method. Once the two areas are separated, the system determines the size of each of them. This proposal was validated through an experiment carried out with OCT 30 images (per eye). The results are encouraging, and the suggestions provided by the system match with medical diagnosis.

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A Computer Vision Approach Based on the Retinal Nerve Fiber Thickness Analysis to Estimate the Risk of Suffering Glaucoma

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