An Improved Blood Vessel Extraction Approach from Retinal Fundus Images Using Digital Image Processing
Blood Vessel Extraction rom Retinal Fundus Images
Keywords:
fundus, diabetic retinopathy, sigmoid function, Gaussian mixture model (GMM), support vector machine (SVM), contrast limited adaptive histogram equalization (CLAHE), Markov’s random field (MRF)Abstract
Diseases like glaucoma, macular degeneration, hypertensive retinopathy and diabetic retinopathy has a major share in complete or partial vision loss in humans. Early diagnose of these diseases is possible through temporal examination of the shape and form, bifurcation patterns and growth of vessels present in the retinal fundus images. In this work, an efficient and rapid scheme to extract the retinal vasculature. In the first step only the green plane is considered and processed out of the RGB color space to accomplish the segmentation process. Contrast enhancement is achieved through applying sigmoid function followed by background exclusion. Finally vessels are extracted through hysteresis thresholding and morphological processing to enhance the fine details in the resultant image. Tradeoff between segmentation accuracy and time consumption of segmentation algorithm is minimized by producing promising accuracy and other metrics. The scheme is tested and evaluated on the retinal fundus images in the databases like STARE and DRIVE. The produced results evaluated and compared to the other state of the art work and proved to be better and outperformed in most cases.
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