Automatic detection and segmentation of optic disc and fovea in retinal image

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Show simple item record Chalakkal, Renoh en Abdulla, Waleed en Thulaseedharan, SS en 2018-12-10T00:02:55Z en 2018-07-31 en
dc.identifier.issn 1751-9659 en
dc.identifier.uri en
dc.description.abstract Feature extraction from retinal images is gaining popularity worldwide as many pathologies are proved having connections with these features. Automatic detection of these features makes it easier for the specialist ophthalmologists to analyse them without spending exhaustive time to segment them manually. The proposed method automatically detects the optic disc (OD) using histogram-based template matching combined with the maximum sum of vessel information in the retinal image. The OD region is segmented by using the circular Hough transform. For detecting fovea, the retinal image is uniformly divided into three horizontal strips and the strip including the detected OD is selected. Contrast of the horizontal strip containing the OD region is then enhanced using a series of image processing steps. The macula region is first detected in the OD strip using various morphological operations and connected component analysis. The fovea is located inside this detected macular region. The proposed method achieves an OD detection accuracy over 95% upon testing on seven public databases and on our locally developed database, University of Auckland Diabetic Retinopathy database (UoA-DR). The average OD boundary segmentation overlap score, sensitivity and fovea detection accuracy achieved are 0.86, 0.968 and 97.26% respectively. en
dc.publisher Institution of Engineering and Technology en
dc.relation.ispartofseries IET Image Processing en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri en
dc.title Automatic detection and segmentation of optic disc and fovea in retinal image en
dc.type Journal Article en
dc.identifier.doi 10.1049/iet-ipr.2018.5666 en
dc.rights.holder Copyright: The author en
dc.rights.accessrights en
pubs.subtype Article en
pubs.elements-id 752715 en Engineering en Department of Electrical, Computer and Software Engineering en Science en Physics en
dc.identifier.eissn 1751-9667 en
pubs.record-created-at-source-date 2018-09-07 en 2018-09-06 en

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