A vessel segmentation technique for retinal images

摘要

Segmentation of the human eye retinal image is an essential step for proper examination and diagnosis by the ophthalmologists or eye care specialists. A technique for vessel segmentation of retinal images is proposed. Retinal images are mostly low-light images, which are first processed for enhancement of light as well as for detail amplification. Illumination of low-light images is enhanced, and details are amplified using content-adaptive filters. For extraction of vessels from retinal images, after low-light and detail enhancement, the B-cosfire filter is modified by including extraction of details and small elements, which may otherwise be ignored. A modified B-cosfire filter is used to extract vessels while minimizing false edges and halo artifacts. The morphological opening is performed to crop vessels that are falsely segmented. The technique is contrasted with other existing methods in terms of accuracy using publicly available datasets. The proposed technique is tested on STARE, CHASE-DB1, and DRIVE databases. The outcome of the proposed procedure has better accuracy, preserved edges, minimum noise, and artifacts than the state-of-the-art techniques.