Automatic Segmentation of Retinal Blood Vessels of Diabetic Retinopathy Patients using Dempster-shafer Edge Based Detector
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Date
2019-06-15
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ANSInet
Abstract
Background and Objective: Diabetic Retinopathy (DR) is a micro-vascular complication of diabetes which results in the alteration or total damage of retinal blood vessels. This is responsible for most partial loss of sight and blindness among diabetic patients across nations of the world. Early examination of retinal blood vessels could help in the detection and diagnosis of the symptoms of DR thereby curtailing its effects. Methodology: Dempster-shafer edge based detector was used to segment retinal blood vessels from retinal images sourced from Digital Retinal Image for Vessel Extraction (DRIVE). Prior to the segmentation, median filter, Contrast Limited Adaptive Histogram Equalization (CLAHE) and mahalanobis distance algorithms were used to preprocess the raw retinal images so that accurate blood vessels detection and segmentation will be achieved. Results: A segmentation accuracy of 0.9765 was recorded when receiver operating characteristics of the technique was computed. This showed that an acceptable degree of blood vessel segmentation was achieved. Furthermore, the segmented blood vessels are publicly available for academic and research purposes.
Conclusion: Dempster-shafer edge based detector has been further shown to be an effective algorithm for blood vessels segmentation in healthy as well as DR retinal images.
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Keywords
Blood vessel, Retina, Diabetes, Diabetic retinopathy, Segmentation
Citation
Akande Noah Oluwatobi, Abikoye Oluwakemi Christiana and Kayode Aderonke Anthonia, 2019. Automatic segmentation of retinal blood vessels of diabetic retinopathy patients using dempster-shafer edge based detector. Asian J. Sci. Res., 12: 376-383.