Title:	LEARNING-BASED SEGMENTATION OF OPTIC DISC IN RETINAL IMAGES USING CLUSTERING TREES AND LOCAL MODE FILTERING
Author(s):	Michael Osadebey, Marius Pedersen and Dag Waaler
ISBN:	978-989-8704-21-4
Editors:	Yingcai Xiao, Ajith P. Abraham and Jörg Roth
Year:	2020
Edition:	Single
Keywords:	Glaucoma, Retinal Images, Optic Disc, Clustering Trees, Local Mode Filtering
Type:	Full
First Page:	124
Last Page:	130
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	Delineation of the optic disc boundary in retinal images is the first step towards the computation of cup-to-disc ratio, an important indicator of ophthalmic pathologies such as glaucoma. This paper proposes the combination of learning-based clustering trees with local mode filtering for the segmentation of the optic disc region in retinal images. The algorithm identifies candidate optic disc region by extracting and pooling low-level features at different clustering resolutions from the filtered region-of-interest in two color channels. Thereafter, we use learned geometric properties such as area, eccentricity and solidity to extract high-level features for the identification of connected components, which most likely belong to the optic disc region. The final stage pools and fully connects these connected components into a single segmented region. Performance evaluation on three publicly available datasets from IDRID, DRISHTI-GS and MESSIDOR demonstrate promising results that are comparable to state-of-the-art algorithms.
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