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Detection of Vascular Intersection in Retina Fundus Image Using Modified Cross Point Number and Neural Network Technique
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2008 (English)Conference paper, (Refereed) Published
Abstract [en]

Vascular intersection can be used as one of the symptoms for monitoring and diagnosis of diabetic retinopathy from fundus images. In this work we apply the knowledge of digital image processing, fuzzy logic and neural network technique to detect bifurcation and vein-artery cross-over points in fundus images. The acquired images undergo preprocessing stage for illumination equalization and noise removal. Segmentation stage clusters the image into two distinct classes by the use of fuzzy c-means technique, neural network technique and modified cross-point number (MCN) methods were employed for the detection of bifurcation and cross-over points. MCN uses a 5x5 window with 16 neighboring pixels for efficient detection of bifurcation and cross over points in fundus images. Result obtained from applying this hybrid method on both real and simulated vascular points shows that this method perform better than the existing simple cross-point number (SCN) method, thus an improvement to the vascular point detection and a good tool in the monitoring and diagnosis of diabetic retinopathy. ©2008 IEEE.

Place, publisher, year, edition, pages
Kuala Lumpur, Malaysia, 2008.
Keyword [en]
Bifurcation, cross point, fundus image, fuzzy c-means, neural network
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:bth-8343ISI: 000259601400049Local ID: oai:bth.se:forskinfo787A38AB87DF09A7C1257506002D47BFISBN: 978-1-4244-1691-2 (print)OAI: oai:DiVA.org:bth-8343DiVA: diva2:836053
Conference
Proceedings of the International Conference on Computer and Communication Engineering 2008, ICCCE08: Global Links for Human Development
Available from: 2012-09-18 Created: 2008-11-19 Last updated: 2015-06-30Bibliographically approved

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CiteExportLink to record
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  • apa
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