Robust and Efficient Segmentation of Blood Vessel in Retinal Images using Gray-Level Textures Features and Fuzzy SVM

Automated techniques for eye diseases identification are very important in the ophthalmology field. Conventional techniques for the identification of retinal diseases are based on manual observation of the retinal components (optic disk, macula, vessels, etc.). This paper presents a new supervised m...

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Bibliographic Details
Published in:Biomedical & pharmacology journal 2015, Vol.8 (2), p.1111-1120
Main Authors: MERLIN, M., SHAN, B.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Automation
Classification
Diabetes
Diabetes mellitus
Diabetic retinopathy
Disease
Eye diseases
Fuzzy logic
Homogenization
Image processing
Medical imaging
Methods
Morphology
Motivation
Retina
Retinopathy
Segmentation
Telemedicine
Veins & arteries
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Summary:Automated techniques for eye diseases identification are very important in the ophthalmology field. Conventional techniques for the identification of retinal diseases are based on manual observation of the retinal components (optic disk, macula, vessels, etc.). This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a Fuzzy logic based Support Vector Machine scheme for pixel organization and computes a 5-D vector composed of gray-level and intensity histogram-based features for pixel representation. The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this intention since they contain retinal images where the vascular structure has been precisely marked by experts. Its effectiveness and robustness with different image conditions, together with its simplicity and fast implementation, make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.
ISSN:0974-6242
2456-2610
DOI:10.13005/bpj/865