This document discusses approaches for glaucoma detection from fundus images. It begins with an introduction to glaucoma as an eye disorder causing vision loss. It describes the need for glaucoma detection as it is a leading cause of blindness. The document outlines different types of glaucoma and common tests used for diagnosis. It then discusses local and global approaches to glaucoma detection from fundus images using techniques like preprocessing, feature extraction, and classification. Finally, it defines performance metrics used to evaluate glaucoma detection methods and provides references for further reading.

1. By
Mr. Kamesh Sonti
Research Scholar,
VIT AP University, Amaravathi
Student ID: 123aaf00e46111e98e78f1ce02410302
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2. CONTENTS
Introduction
Need for Glaucoma Detection
Types of Glaucoma
Approaches to Glaucoma Detection
Image Processing Techniques
Performance Metrics
References
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3. INTRODUCTION
 Glaucoma is an eye disorder that causes irreversible loss of vision. It
affects the Optic Nerve in retina.
 Glaucoma normally occurs with increased intra-ocular pressure (IOP)
in eyes and gradually damages the vision of eyes.
Courtesy by: IEEE Journal of Biomedical and Health Informatics
Fig-1: Fundus Images : (a) Normal (b) Glaucoma
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4. NEED FOR GLAUCOMA DETECTION
 Leading cause of blindness worldwide
• In India – 2nd leading cause of blindness
• It is estimated to affect 79 million people in the world by the year 2020
 Prevalent in aging population
• India alone hosts 20% of glaucoma cases
 Irreversible loss of vision
• Thus timely detection required
 Lack of manpower in terms of skilled technicians
• Thus computer aided solutions for screeningCC BY-SA-NC 4

5. EARLY SIGNS OF GLAUCOMA
 Loss of peripheral or side vision
 Seeing halos around lights
 Redness in the eye
 Eye that looks hazy
 Nausea or vomiting
 Pain in the eye and in the head
 Narrowing of vision (tunnel vision)
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6. TYPES OF GLAUCOMA
 Open Angle Glaucoma:
-- The angle in your eye where the iris meets the cornea is as wide and open causing an
increase in internal eye pressure and subsequent damage to the optic nerve.
 Acute Angle Glaucoma:
-- Caused by a rapid or sudden increase in intraocular pressure (IOP), the pressure within
the eye. Fluid is continually produced inside, and drains out of, the normal eye.
Fig-2: (a) Open Angle Glaucoma (b) Closure GlaucomaCC BY-SA-NC 6

7. ANATOMY OF GLAUCOMA EYE
Courtesy by: www.imagessure.comCC BY-SA-NC 7

8. COMMON GLAUCOMA TESTS
 Five factors should be checked before making a glaucoma diagnosis:
The inner eye pressure
 The shape and color of the optic nerve
 The complete field of vision
 The angle in the eye where the iris meets the cornea
 Thickness of the cornea
 Regular glaucoma check-ups include two routine eye tests:
Tonometry
 Ophthalmoscopy
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9.  Retinal images taken using Fundus
camera.
 Specialized low power microscope
with an attached camera.
 Photographs the interior surface of
eye.
FUNDUS IMAGES
Courtesy by: en.wikipedia.orgCC BY-SA-NC 9

10. APPROACHES TO GLAUCOMA DETECTION
 Local Approach:
-- Aim at measuring the cup to disc ratio after segmenting the cup and
disk regions.
Retinal Fundus
Images
Preprocessing
Optic
Disc
Optic
Cup
Neuro Retinal Rim
Cup-Disc Ratio
> 0.5 < 0.5
Glaucoma NormalCC BY-SA-NC 10

11.  Global Approach:
-- Aims at deriving global image features. Various factors including color,
intensity and texture are computed.
-- Supervised learning is used to separate normal and diseased images.
Retinal Fundus Images
Preprocessing
Feature Extraction
Classification
Glaucoma Normal
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12. IMAGE PROCESSING TECHNIQUES
 Image Enhancement
-- Includes filtering and histogram equalization.
 Image Registration
-- In medical diagnosis, it is essential to combine data from different images and
for better analysis and measurements images are aligned geometrically.
 Image Fusion
-- A process of combining information acquired from number of imaging devices.
 Feature Extraction
-- Process of identifying and extracting region of interest from the image.
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13.  Segmentation
-- To extract various features of image which can be merged or split in order to
build object of interest on which analysis and interpretation can be performed.
 Morphology
-- A collection of non-linear processes which can be applied to an image to
remove details smaller than a certain reference shape. Various morphological
operation are erosion, dilation, opening and closing.
 Classification
-- It includes labeling of a pixel or group of pixels based on the gray values and
other statistical parameters.
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14. PERFORMANCE METRICS
 True positive (TP): Number images detected as glaucoma by an expert and the proposed method.
 True negative (TN): Number of images detected as normal by an expert and the proposed method.
 False positive (FP): Number of images detected as normal by an expert but detected as glaucoma
by the proposed method.
 False negative (FN): Number of images detected as glaucoma by an expert but detected as normal
by the proposed method. CC BY-SA-NC 14

15. REFERENCES
1. Shishir Maheshwari, Ram Bilas Pachori, Member, IEEE, and U. Rajendra Acharya, Senior
Member, IEEE , “Automated diagnosis of glaucoma using empirical wavelet transform and
correntropy features extracted from fundus images”, IEEE Journal of Biomedical and Health
Informatics, March-2016.
2. U. R. Acharya et al., “Decision support system for the glaucoma using Gabor transformation”,
Biomed. Signal Process. Control, vol. 15, pp. 18–26, 2015.
3. Y. C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, and C. Y. Cheng, “Global prevalence
of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-
analysis”, Ophthalmology, vol. 121, no. 11, pp. 2081–2090, 2014.
4. J. Gilles, G. Tran, and S. Osher, “2D empirical transforms. Wavelets, ridgelets, and curvelets
revisited”, SIAM J. Imag. Sci., vol. 7, no. 1, pp. 157–186, 2014.
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16. 5. U. R. Acharya, S. Dua, X. Du, S. V. Sree, and C. K. Chua, “Automated diagnosis of glaucoma
using texture and higher order spectra features”, IEEE Trans. Inf. Technol. Biomed., vol. 15,
no. 3, pp. 449–455, May. 2011.
6. R. Bock, J. Meier, L. G. Nyl, J. Hornegger, and G. Michelson, “Glaucoma risk index:
Automated glaucoma detection from color fundus images”, Medical Image Analysis, vol. 14,
pp. 471–481, June 2010.
7. R. Bock, J. Meier, L. G. Nyul, J. Hornegger, and G. Michelson, “Glaucoma risk index:
Automated glaucoma detection from color fundus images”, Med. Image Anal., vol. 14, no. 3,
pp. 471–481, 2010.
8. L. G. Nyul, “Retinal image analysis for automated glaucoma risk evaluation”, Proc. SPIE, vol.
7497, pp. 1–9, 2009.
9. R. Kolar and J. Jan, “Detection of glaucomatous eye via color fundus images using fractal
dimensions”, Radio Eng., vol. 17, no. 3, pp. 109–114, 2008.
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17. ACKNOWLEDGEMENT
My sincere acknowledgement to:
ACADEMIC WRITING
(UGC MOOC Course)
Course Coordinator:
Dr.Ajay Semalty
H.N.B.Garhwal University, Uttarakhand
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18. THANK YOU
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