Cascaded Feature Vector Assisted Blood Vessel Segmentation from Retinal Images

Blood Vessels have a significant role in the diagnosis of Diabetic Retinopathy (DR) through retinal images. However, the major issues are the accurate segmentation of blood vascular structure from the retinal image. As there exist tiny vessels in the retina at the advanced stages of DR, the extraction of such kind of vessels is a challenging task. Hence, this paper proposes a new retinal vasculature segmentation mechanism based on pixel-wise classification. A new feature vector called as Cascaded Feature Vector (CFV) is introduced here to represent each pixel with a set of composite features. To extract such features, this approach totally employs five different filters namely Edge (E), Morphology (M), Statistical (S), Hessian (H), and Gradient (G) filters. Based on obtained features, CFV is formulated and fed to machine learning algorithms for classification. Artificial Neural Networks (ANN) and Support Vector Machine (SVM) are employed for classification. Experimental validation on the two datasets namely DRIVE, and ARIA proves the effectiveness of the proposed method in terms of segmentation accuracy.