Automated Method for Optic Disc Detection and Elimination in Digital Fundus Images
Parashuram Bannigidad1, Asmita Deshpande2
1Parashuram Bannigidad, Department of Computer Science, Rani Channamma University, Belagavi, Karnataka, India.
2Asmita Deshpande*, Department of Computer Science, Belagavi, Karnataka, India.
Manuscript received on November 17., 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 12558-12563 | Volume-8 Issue-4, November 2019. | Retrieval Number: D6829118419/2019©BEIESP | DOI: 10.35940/ijrte.D6829.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Localizing, segmenting and eliminating the optic disc region of a fundus image is a prerequisite task in the automatic investigation of a number of retinal diseases such as Diabetic retinopathy, Glaucoma, Macular Edema, etc. Accurate detection of optic disc is a challenging task due to a number of reasons. Optic disc in most fundus images does not exhibit clear disc boundaries and there are number of blood vessels crossing it. An important task in automated retinal image analysis system is the detection and elimination of optic disc because the lesion regions in diabetic retinopathy closely resemble the color and texture of an optic disc. Hence, eliminating the optic disc region can improve the performance of diabetic retinopathy detection. The proposed work presents a novel method for optic disc segmentation which is not restricted by the location of the optic disc on the retina. The proposed algorithm localizes the position of the optic disc that is independent of its location and dynamically finds its center. The proposed method is tested on images from DRISHTI-GS, DIARETDB1, DRIONS-DB and DRIVE databases based on morphological operation and finding the largest connected component. The precision values of segmentation for digital fundus images from DRISHTI-GS, DIARETDB1, DRIONS-DB, and DRIVE databases are 0.98, 0.99, 0.98 and 0.99 respectively using the proposed method. The algorithm has yielded consistent high values of precision and recall indicating its robustness and efficiency.
Keywords: Fundus Image, Optic Disc, Morphological Operations, Diabetic Retinopathy, Segmentation, Active Contour, Blood Vessels.
Scope of the Article: Digital Clone or Simulation.