Muhammad Abdullah1, Muhammad Moazam Fraz2 and Sarah A. Barman2
1 School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Sector H-12, Islamabad, 44000, Pakistan
2 Faculty of Science Engineering and Computing, Kingston University London, KT12EE, United Kingdom
Automated retinal image analysis has been emerging as an important diagnostic tool for early detection of eye related disease such as glaucoma and diabetic retinopathy. In this paper, we have presented a robust methodology for optic disc detection and boundary segmentation, which can be seen as the preliminary step in the development of a computer-assisted diagnostic system for glaucoma in retinal images. The proposed method is based on morphological operations, Circular Hough transform and Grow Cut algorithm. The morphological operators are used to enhance the optic disc and remove the retinal vasculature and other pathologies. The optic disc center is approximated using the Circular Hough Transform, and the Grow Cut algorithm is employed to precisely segment the optic disc boundary. The method is quantitatively evaluated on five publicly available retinal image databases DRIVE, DIARETDB1, CHASE_DB1, DRIONS-DB, Messidor and one local Shifa Hospital Database. The method achieves optic disc detection success rate as 100% for these databases with the exception of 99.09% and 99.25% for the DRIONS-DB, Messidor, and ONHSD databases respectively. The optic disc boundary detection achieved an average spatial overlap of 78.6%, 85.12%, 83.23%, 85.1%, 87.93%, 80.1%, and 86.1% respectively for these databases. This unique method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc.
Glaucoma Detection, Growcut Algorithm, Image Analysis, Optic Disc, Retinal Image Analysis