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Proceedings ArticleDOI

Glaucoma detection from fundus images using MATLAB GUI

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TLDR
Threshold type segmentation method is used in this system for localizing the optic disc and optic cup and the ratio of the cup and disc diameter is called cup-to-disc ratio (CDR).
Abstract
A troublesome disease in which damages of the optic nerve of eye's is nothing but the glaucoma, which causes irretrievable loss of vision. Glaucoma is a disease where if treatment is get late, the person can blind. Normally glaucoma detects when there is an increase in the fluid in the front of eye. When that extra fluid is increased, the pressure in your eye is also getting increased. Accordingly, the size of the optic disc and optic cup is increased as a result diameter also increased. The ratio of the cup and disc diameter is called cup-to-disc ratio (CDR). Threshold type segmentation method is used in this system for localizing the optic disc and optic cup. Another edge detection and ellipse fitting algorithm are also used. The proposed system for optic disc and optic cup localization and CDR calculation is MATLAB GUI software.

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Citations
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Journal ArticleDOI

An automated glaucoma screening system using cup-to-disc ratio via Simple Linear Iterative Clustering superpixel approach

TL;DR: An automatic glaucoma screening system based on superpixel classification by providing a high-quality input image is developed and the experimental results have successfully distinguished optic disc and optic cup from the background with an average accuracy and sensitivity.
Proceedings ArticleDOI

Glaucoma Detection Using Fundus Images of The Eye

TL;DR: A computational tool for automatic glaucoma detection is presented, improvements for disc segmentation in comparison with other works on the literature, a novel method to segment the cup by thresholding and a new measure between the size of the cup and thesize of the disc are reported.
Journal ArticleDOI

Automatic Glaucoma Classification Using Residual Network Architecture

TL;DR: In this article , a system that can classify glaucoma through fundus images using the Convolutional Neural Network (CNN) with Residual Network-34 is presented.
Proceedings ArticleDOI

Glaucoma Detection using HOG and Feed-forward Neural Network

TL;DR: In this paper , a feature extraction method called Histogram of Oriented Gradients (HOG) and feed-forward neural network was used as a classifier to classify the images.
References
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Book

Fundamentals of digital image processing

TL;DR: This chapter discusses two Dimensional Systems and Mathematical Preliminaries and their applications in Image Analysis and Computer Vision, as well as image reconstruction from Projections and image enhancement.
Journal ArticleDOI

Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment

TL;DR: An automatic OD parameterization technique based on segmented OD and cup regions obtained from monocular retinal images and a novel cup segmentation method which is based on anatomical evidence such as vessel bends at the cup boundary, considered relevant by glaucoma experts are presented.

A Comprehensive Retinal Image Dataset for the Assessment of Glaucoma from the Optic Nerve Head Analysis

TL;DR: A comprehensive dataset of retinal images of both normal and glaucomatous eyes with manual segmentations from multiple human experts with expert opinion is presented to aid benchmarking of new methods.
Proceedings ArticleDOI

Optic cup and disk extraction from retinal fundus images for determination of cup-to-disc ratio

TL;DR: The threshold and variational level set methods produced 97% accuracy in the determined CDR results, an 18% improvement over the color intensity method, which indicates potential applicability of the methods for automated and objective mass screening for early detection of glaucoma.
Proceedings ArticleDOI

Early detection of glaucoma in retinal images using cup to disc ratio

TL;DR: The component analysis method and region of interest (ROI) based segmentation are used for the detection of disc and the active contour is used to plot the boundary accurately.
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