Automated Glaucoma Screening in Retinal Fundus Images

Abstract Glaucoma is an eye disease which damages the optic nerve that carries information from the eye to the brain. Glaucoma is the second foremost reason of blindness. An efficient hardware based implementation of glaucoma screening is a significant task in the automated retinal image analysis method. This paper presents an automatic glaucoma screening using a TMS320C6416DSK DSP board. The detection procedure consists of two stages. The first stage comprises of image pre-processing and detection of optic nerve head center using circular Hough Transform .In the second stage, the optic disk diameter is calculated and cup is segmented from disk. The proposition between disk and cup is calculated for abnormal image screening. The implemented technique is tested on a publicly available retinal image data sets and the average accuracy achieved is 97.5%. Keywords: fundus image, glaucoma, optic disk, circular hough transform, TMS320c6416 1. Introduction Glaucoma is an irreversible eye syndrome. According to reports in 2010, it is second primary reason of blindness in the world. 2.3% of the peoples got affected from total population. It is predicted that this number will increase to 2.86% in 2020.Studies have been shown that increase in intraocular pressure (IOP) of the eye is one the cause for glaucoma [2]. To maintain healthy vision, eye produces a small amount fluid called aqueous humor the same amount fluid will be thrown out of eye. This balance keeps the IOP in limit. If the balance is not maintained the IOP increase and damage the optic nerve head which make irreversible vision loss. So, the early precise detection and treatment of glaucoma will control the progression of the disease. The ophthalmologist uses the ratio between optic cup and optic disk (CDR) as a parameter to screen the glaucoma patients. The CDR is <0.65 in case of normal eye and for the glaucoma eye the value is lies in between 0.65 to 0.9. Manual glaucoma screening is time consuming and prone to human error. So, An intricate algorithm is needed for mass screening of glaucoma.Figure 1 shows the normal image and glaucoma image.

[1]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.

[4]  Yazhu Chen,et al.  A Computer-based Diagnosis System for Early Glaucoma Screening , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[5]  H. Quigley,et al.  The number of people with glaucoma worldwide in 2010 and 2020 , 2006, British Journal of Ophthalmology.

[6]  Tien Yin Wong,et al.  Focal edge association to glaucoma diagnosis , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Jack J. Kanski,et al.  Comprar Kanski. Clinical Ophthalmology: A Systematic Approach, 7th Edition | Jack J. Kanski | 9780702040931 | Butterworth Heinemann , 2011 .

[8]  S. S. Chaudhuri,et al.  Optical cup to disc ratio measurement for glaucoma diagnosis using harris corner , 2012, 2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT'12).

[9]  Jamshid Shanbehzadeh,et al.  Automatic measurement of cup to disc ratio for diagnosis of glaucoma on retinal fundus images , 2013, 2013 6th International Conference on Biomedical Engineering and Informatics.

[10]  S.Karthikeyan,et al.  Clustering Based Optic Disc and Optic CupSegmentation for Glaucoma Detection , 2014 .

[11]  Pg Student.  Clustering Based Optic Disc and Optic Cup Segmentation for Glaucoma Detection , 2014 .

[12]  Samina Khalid,et al.  Review of Machine Learning techniques for glaucoma detection and prediction , 2014, 2014 Science and Information Conference.

[13]  S. Aruna,et al.  Automated glaucoma detection system based on wavelet energy features and ANN , 2014, 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[14]  Tzyy-Ping Jung,et al.  Glaucoma Progression Detection Using Structural Retinal Nerve Fiber Layer Measurements and Functional Visual Field Points , 2014, IEEE Transactions on Biomedical Engineering.

[15]  Ramesh R. Manza,et al.  Development of primary glaucoma classification technique using optic cup & disc ratio , 2015, 2015 International Conference on Pervasive Computing (ICPC).