An Efficacious Method of Cup to Disc Ratio Calculation for Glaucoma Diagnosis Using Super pixel

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Abstract
Glaucoma is a chronic eye disease that leads to vision
loss. Since it cannot be cured, detecting the disease in time is
important. The tests which are done to detect Glaucoma using
intraocular pressure (IOP) are not sensitive enough for
population based glaucoma screening. The assessment of Optic
nerve head damage in retinal fundus images is both more
promising and superior. In this paper the optic disc and optic
cup segmentation using Superpixel classification for glaucoma
screening. The SLIC (Simple Linear Iterative Clustering)
algorithm is incorporated to segment the fundus retinal image
into compact and nearly uniform superpixels. Unlike dividing
an image into a grid of regular pixels, superpixels have the
important property of preserving local boundaries. The
segmented optic disc and optic cup are then used to compute
the cup to disc ratio for glaucoma screening. The Cup to Disc
Ratio (CDR) of the color retinal fundus camera image is the
primary identifier to confirm Glaucoma for a given patient.
Superpixels are becoming increasingly popular in computer
vision applications because of improved performance over
pixel-based methods.
Keywords:intraocular pressure (IOP), superpixel, cup to
disc ratio (CDR).
I.Introduction
Glaucoma is a group of eye diseases characterized by
damage to the optic nerve. It is an eye disease which causes
irreversible loss of vision. In its early stages, glaucoma may
present few or no symptoms and can gradually steal sight. In
fact, most people affected by Glaucoma do not know if they
have the disease or not. If left undetected and untreated,
glaucoma can lead to blindness. One of the high risk factors for
glaucoma is elevated Intraocular pressure (IOP), or pressure
inside the eye. A healthy and a normal eye secrete a fluid
named aqueous humor, at the same rate at which it drains. The
pressure is increased when the draining system is blocked and
the fluid cannot exit at a normal rate. This increased IOP pushes
against the optic nerve causing gradual damage, which may
result in vision loss, usually starting with the peripheral, or side
vision.
Increased eye pressure is often associated with gradual damage
to the nerve fibers that make up the optic nerve. In this paper
optic disc and optic cup are segmented using superpixel
classification for detecting Glaucoma. For automatic optic
nerve head assessment we can use the image features for a
binary classification between glaucomatous and healthy
subjects. These features are normally computed at the imagelevel.
CDR is commonly used because of its accuracy and
simplicity. When CDR is greater than 0.65,then it indicates a high risk of glaucoma [10]. However,
because 3-D images are not easily available, 2-D color fundus
images are still referred to by most clinicians. Moreover, the
high cost of obtaining, 3-D images make it inappropriate for a
large-scale screening program. This paper focuses on automatic
glaucoma screening using CDR from 2-D fundus images. The
CDR is computed as the ratio of the vertical cup diameter
(VCD) to vertical disc diameter (VDD) clinically.
PROBLEM STATEMENT:
An early detection of glaucoma is particularly
significant since it allows timely treatment to prevent major
visual field loss and prolongs the effective years of usable
vision. The diagnosis of glaucoma can be done through
measurement of CDR (cup-to-disc ratio). Currently, CDR
evaluation is manually performed by trained ophthalmologists
or expensive equipment such as Heidelberg Retinal
Tomography (HRT). However, CDR evaluation by an
ophthalmologist is subjective and the availability of HRT is
very limited. Thus, this paper proposes an intuitive, efficient
and objective method for automatically classifying digital fundus images into either normal or glaucomatous types in
order to facilitate ophthalmologists.
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