Abstract
Automated retinal image analysis has been emerging as an important diagnostic tool for early detection of eye-related diseases 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, the circular Hough transform and the 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 an optic disc detection success rate of 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.
Highlights
Digital retinal images are widely used for early detection of retinal, ophthalmic and systemic diseases because they provide a non-invasive window to the human circularity system and associated pathologies
This paper presents a new approach for automatic detection and segmentation of the Optic Disc (OD) based on morphological operations, Circular Hough Transform (CHT) (Illingworth & Kittler 1987) and Grow Cut (GC) algorithm (Vezhnevets & Konouchine 2005)
Experimental evaluation shows that this method is computationally fast in processing, robust to the variation in image contrast and illumination, and comparable with the state-of-the-art methodologies in terms of quantitative performance metrics
Summary
Digital retinal images are widely used for early detection of retinal, ophthalmic and systemic diseases because they provide a non-invasive window to the human circularity system and associated pathologies Glaucoma and Diabetic Retinopathy (DR) are among major retinal disease which are the leading cause of vision loss and blindness in the working population (Federation 2013). Detection of these disease by screening programs and subsequent treatment can prevent blindness. OD detection is preliminary step for glaucoma screening, which is globally the second leading cause of blindness. It helps in the detection and localization of other retinal structures which includes the fovea, macula and estimating vascular changes (Basit & Fraz 2015).
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