Abstract
Automatic segmentation of the vasculature in retinal images is important in the detection of diabetic retinopathy that affects the morphology of the blood vessel tree. In this paper, a hybrid method for efficient segmentation of multiple oriented blood vessels in colour retinal images is proposed. Initially, the appearance of the blood vessels are enhanced and background noise is suppressed with the set of real component of a complex Gabor filters. Then the vessel pixels are detected in the vessel enhanced image using entropic thresholding based on gray level co-occurrence matrix as it takes into account the spatial distribution of gray levels and preserving the spatial structures. The performance of the method is illustrated on two sets of retinal images from publicly available DRIVE (Digital Retinal Images for Vessel Extraction) and Hoover’s databases. For DRIVE database, the blood vessels are detected with sensitivity of 86.47±3.6 (Mean±SD) and specificity of 96±1.01.
Highlights
In clinical ophthalmology colour retinal images acquired from digital fundus camera are widely used for detection and diagnosis of diseases like diabetic retinopathy, hypertension and various vascular disorders
Automatic segmentation of the vasculature in retinal images is important in the detection of diabetic retinopathy that affects the morphology of the blood vessel tree
The vessel pixels are detected in the vessel enhanced image using entropic thresholding based on gray level co-occurrence matrix as it takes into account the spatial distribution of gray levels and preserving the spatial structures
Summary
In clinical ophthalmology colour retinal images acquired from digital fundus camera are widely used for detection and diagnosis of diseases like diabetic retinopathy, hypertension and various vascular disorders. The timely diagnosis and referral for management of diabetic retinopathy can prevent 98% of severe visual loss, for that, the patient has to undergo regular screening of eye for retinopathy. An automatic segmentation of the vasculature could save workload of the ophthalmologists and may assist in characterizing the detected lesions and to identify false positives [2]. Another important application of automatic retinal vessel segmentation is in the registration of retinal images of the same patient taken over period of time [3]. The registered images are useful in monitoring the progression of disease and to observe the effect of treatment
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