dc.contributor.author | Osadebey, Michael | |
dc.contributor.author | Pedersen, Marius | |
dc.contributor.author | Waaler, Dag | |
dc.date.accessioned | 2021-01-29T09:34:34Z | |
dc.date.available | 2021-01-29T09:34:34Z | |
dc.date.created | 2020-11-30T14:04:16Z | |
dc.date.issued | 2020 | |
dc.identifier.isbn | 978-989-8704-21-4 | |
dc.identifier.uri | https://hdl.handle.net/11250/2725298 | |
dc.description.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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IADIS Press | en_US |
dc.relation.ispartof | Proceedings of the IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing | |
dc.title | Learning-based segmentation of optic disc in retinal images using clustering trees and local mode filtering | en_US |
dc.type | Chapter | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.pagenumber | 124-130 | en_US |
dc.identifier.cristin | 1854206 | |
dc.description.localcode | This chapter will not be available due to copyright restrictions (c) 2020 by IADIS Press | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |