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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.

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