Citation

Nasiruddin, Z. H. and Wan Zaki, Wan Mimi Diyana and Hudaibah, S. Assyareefah and Amir Hamzah, Nur Asyiqin (2022) Automated Retinal Blood Vessel Feature Extraction in Digital Fundus Images. In: 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 13-15 September 2022, Kota Kinabalu, Malaysia.

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Abstract

The retinal microvascular network manifests the well-being of other systems and organs as they are structurally and physiologically similar. It offers a unique window to assess numerous disorders such as hypertension, heart disease and nervous system illnesses. However, manually analysing retinal blood vessels in digital fundus images is challenging. In addition, the low contrast images limit the diagnosis of retinal blood vessel-related eye diseases. Thus, this work uses the digital image processing approach to automate the extraction and selection of significant blood vessel features, i.e., the width and pixel intensity of the artery and vein. The digital fundus images are collected from the Digital Retinal Images for Vessel Extraction (DRIVE) database, consisting of twenty 584×565-pixel digital fundus and ground truth images. The proposed method automatically extracts the retinal width and intensity based on the identified coordinates of the blood vessel's skeleton images. Using a one-way ANOVA statistical test computation, we found that the width and the green channel intensity pixel are significant features (p-value <0.005) that can be used to differentiate artery and vein in digital fundus images.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Feature extraction, feature selection, fundus image, Retinal microvascular, Central Retinal Artery Equivalent (CRAE), Central Retinal Vein Equivalent (CRVE), arteriolar-to-venular diameter ratio (AVR), blood vessel segmentation, frangi filter, B-COSFIRE filter
Subjects:	R Medicine > RE Ophthalmology
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	09 Dec 2022 02:56
Last Modified:	09 Dec 2022 02:56
URII:	http://shdl.mmu.edu.my/id/eprint/10807

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