Automated segmentation of optic disc using statistical region merging and morphological operations


Accurate Optic Disc (OD) segmentation is vital in designing systems that aid the diagnosis and evaluation of early phases of retinal diseases. However, in many images, the OD boundary is ambiguous, which makes the automated OD segmentation process very challenging. A method to segment OD based on statistical region merging and morphological operations is proposed in this paper. The proposed method is tested on standard databases MESSIDOR, DIARETDB1, DIARETDB0, and DRIONS-DB. The average overlap ratios are found to be 91.35% for DIARETDB1 images, 88.80% for DRIONS-DB images, 86.60% for DIARETDB0 images and 89.68% for MESSIDOR images, with average accuracies of 99.68%, 99.89%, 99.69%, and 99.93% respectively. A comparison with alternative methods showed that the proposed algorithm in OD segmentation is better than existing ones.

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Correspondence to Varun P. Gopi.

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Nija, K.S., Anupama, C.P., Gopi, V.P. et al. Automated segmentation of optic disc using statistical region merging and morphological operations. Phys Eng Sci Med (2020).

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  • Optic disc
  • Statistical region merging
  • Diabetic retinopathy
  • Morphological operations
  • OD segmentation