Application of grow cut algorithm for localization and extraction of optic disc in retinal images

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 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 grow cut algorithm is employed to precisely segment the optic disc boundary. The method is quantitatively evaluated on publicly available retinal image databases DRIVE, DIARETDB1 and CHASE_DB1 and has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc. The proposed method has optic disc detection success rate of 100% for these databases. The optic disc boundary detection achieved an average spatial overlap of 77.10%, 85.12% and 83.23% for these databases, respectively, which are higher than currents algorithms.