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Artery/vein classification of retinal vessels using classifiers fusion

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Abstract

The morphological changes in retinal blood vessels indicate cardiovascular diseases and consequently those diseases lead to ocular complications such as Hypertensive Retinopathy. One of the significant clinical findings related to this ocular abnormality is alteration of width of vessel. The classification of retinal vessels into arteries and veins in eye fundus images is a relevant task for the automatic assessment of vascular changes. This paper presents an important approach to solve this problem by means of feature ranking strategies and multiple classifiers decision-combination scheme that is specifically adapted for artery/vein classification. For this, three databases are used with a local dataset of 44 images and two publically available databases, INSPIRE-AVR containing 40 images and VICAVR containing 58 images. The local database also contains images with pathologically diseased structures. The performance of the proposed system is assessed by comparing the experimental results with the gold standard estimations as well as with the results of previous methodologies, achieving promising classification performance, with an over all accuracy of 90.45%, 93.90% and 87.82%, in retinal blood vessel separation for Local, INSPIRE-AVR and VICAVR dataset, respectively.

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Acknowledgements

The author would like to thank Dr. Umer Salman from Hameed Latif Hospital, Lahore, Pakistan for assisting in providing ground truth for retinal vessel classification.

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  1. Samra Irshad has made the same contribution in writing this paper as Xiao-Xia Yin.

Authors and Affiliations

  1. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China

    Xiao-Xia Yin

  2. Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia

    Samra Irshad & Yanchun Zhang

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Yin, XX., Irshad, S. & Zhang, Y. Artery/vein classification of retinal vessels using classifiers fusion. Health Inf Sci Syst 7, 26 (2019). https://doi.org/10.1007/s13755-019-0090-4

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