An Efficient ELM Approach for Blood Vessel Segmentation in Retinal Images

Diabetic Retinopathy (DR) is one of the most important ophthalmic pathological reasons of blindness among people of working age. Previous techniques for blood vessel detection in retinal images can be categorized into rule-based and supervised methods. This research presents a new supervised technique for blood vessel detection in digital retinal images. This novel approach uses an Extreme Learning Machine (ELM) approach for pixel classification and calculates a 7-D vector comprises of gray-level and moment invariants-based features for pixel representation. The approach is based on pixel classification using a 7-D feature vector obtained from preprocessed retinal images and given as input to a ELM. Classification results (real values between 0 and 1) are thresholded to categorize each pixel into two classes namely vessel and nonvessel. Ultimately, a post processing fills pixel gaps in detected blood vessels and eliminates falsely-detected isolated vessel pixels. The technique was assessed on the publicly available DRIVE and STARE databases, widely used for this purpose, as they comprises of retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The approach proves particularly accurate for vessel detection in STARE images. Its efficiency and strength with different image conditions, along with its simplicity and fast implementation, make this blood vessel segmentation proposal appropriate for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.