Extraction of vasculature map of color retinal fundus image

Abstract

This paper presents an unsupervised and novel method for extraction of vasculature map from colored retinal fundus images. Proposed technique makes use of a fusion of bimodal masking and global thresholding technique for extraction of vessels. For this, adaptive histogram equalization method is used for enhancement of the retinal input fundus images while, on the other hand, an average filter is used on the masked images to remove the noise from the image. At this stage, bimodal masking is used to generate the masked image to exclude the pixels that belong to the background. The use of this technique reduces the analysis time and computational effort as operations will be focused only on the object pixels. After that, a global thresholding technique is utilized on the masked foreground image, which produces a vasculature map with border. Since the border has no concern in our system, hence, it is removed using the mask generated through bimodal masking. Fundus images of DRIVE and STARE database is used to perform extensive computations. The results are encouraging as the proposed system shows better outcomes on 3-quality measures: average sensitivity, specificity, and accuracy which comes out to be (84.18%, 96.68% and 95.68%) and (81.79%, 90.74% and 90.08%) for DRIVE and STARE database respectively. The results prove that the proposed methodology is capable of extracting the vasculature map accurately, which can be further helpful for diagnosis, screening, and treatment of various disorders of fundus images.