PURPOSE

The optic disc is the origin of the optic nerve, where the axons of retinal ganglion cells join together. The size, shape and contour of optic disc are used for classification and identification of retinal diseases. Automatic detection of eye disease requires development of an efficient algorithm. This paper proposes an efficient method for optic disc segmentation and detection for the diagnosis of retinal diseases.

METHODS

The methodology involves optic disc localization, blood vessel inpainting and optic disc segmentation. Localization is based on principal component analysis, and segmentation is based on Markov random field segmentation. In order to get reasonable background images, blood vessel inpainting is done before segmentation.

RESULTS

The proposed method tested with two standard databases MESSIDOR and DRIVE, and achieved an average overlapping score of 92.41, 92.17%, respectively; also validation experiments were done with one local database from Venu Eye Hospital, New Delhi, and obtained an average overlapping score of 91%.

CONCLUSION

An efficient algorithm is developed for detecting optic disc using principal component analysis-based localization and Markov random field segmentation. The comparison with alternative method yielded results that demonstrate the superiority of the proposed algorithm for optic disc detection.