Blood vessel inpainting based technique for efficient localization and segmentation of optic disc in digital fundus images. (March 2016)

• Record Type: Journal Article
• Authors: Sarathi, M. Partha; Dutta, Malay Kishore; Singh, Anushikha; Travieso, Carlos M.
• Is Part Of: Biomedical signal processing and control. Volume 25(2016)
• Page Start: 108
• Page End: 117
• Publication Date: 2016-03
• Subjects: Fundus image -- Optic disc -- Blood vessel -- In-painting -- Region growing -- Spline interpolation; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.10.012
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The proposed work presents a novel framework for fast and fully automatic detection of OD in fundus images. Adaptive threshold based Region Growing technique followed by strategic blood vessel inpainting is used for OD segmentation. The proposed technique achieved overlapping ratio of 89% and 87% for standard databases MESSIDOR and DRIVE respectively. Experiments were done on a labeled dataset obtained from a local eye hospital achieving 91% average OD segmentation accuracy. Abstract: The Optic disc (OD) nerve head region in general and OD center coordinates in particular form basis for study and analysis of various eye pathologies. The shape, contour and size of OD is vital in classification and grading of retinal diseases like glaucoma. There is a need to develop fast and efficient algorithms for large scale retinal disease screening. With this in mind, this paper present a novel framework for fast and fully automatic detection of OD and its accurate segmentation in digital fundus images. The methodology involves optic disc center localization followed by removal of vascular structure by accurate inpainting of blood vessels in the optic disc region. An adaptive threshold based Region Growing technique is then employed for reliable segmentation of fundus images. The proposed technique achieved significant results when tested on standard test databases like MESSIDOR and DRIVE with average overlapping ratio of 89% and 87%, respectively. Validation experiments were done on a labeled dataset containing healthy and pathological images obtained from a local eye hospital achieving an appreciable 91% average OD segmentation accuracy.