Abstract
Diabetic Retinopathy (DR) harm retinal blood vessels in the eye causing visual deficiency. The appearance and structure of blood vessels in retinal images play an essential part in the diagnoses of an eye sicknesses. We proposed a less computational unsupervised automated technique with promising results for detection of retinal vasculature by using morphological hessian based approach and region based Otsu thresholding. Contrast Limited Adaptive Histogram Equalization (CLAHE) and morphological filters have been used for enhancement and to remove low frequency noise or geometrical objects, respectively. The hessian matrix and eigenvalues approach used has been in a modified form at two different scales to extract wide and thin vessel enhanced images separately. Otsu thresholding has been further applied in a novel way to classify vessel and non-vessel pixels from both enhanced images. Finally, postprocessing steps has been used to eliminate the unwanted region/segment, non-vessel pixels, disease abnormalities and noise, to obtain a final segmented image. The proposed technique has been analyzed on the openly accessible DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the REtina) databases along with the ground truth data that has been precisely marked by the experts.
Highlights
Diabetic Retinopathy (DR) is a severe disease and is one of the main source of visual impairment among adults aged 20–74 years in the United States [1]
We have analyzed the performance of retinal vessel segmentation methods on the DRIVE [45] and the STARE [8] databases
The proposed framework has beenapplied on 20 test images of the DRIVE and the STARE datasets
Summary
DR is a severe disease and is one of the main source of visual impairment among adults aged 20–74 years in the United States [1]. Segmentation and review of retinal vasculature characteristics for example, tortuosity, normal or abnormal branching, shading and diameter as well as the optic disk morphology permits eye care experts and ophthalmologists to perform mass vision screening exams for early discovery of retinal ailments and treatment assessment This could forestall and decrease vision debilitations, age-related diseases, and numerous cardiovascular ailments, and in addition diminish the expense of the screening [4, 5]. Manual segmentation and estimation procedures can take up to an hour for assessment of just a single eye In this way, a completely automated framework extracting the vessel structures in retinal images could surely diminish the workload of eye clinicians. Segmentation and assessment procedures themselves need a large computational endeavors
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