Retinal Vessel Segmentation Based on Multiscale Line Detection and Morphological Transformation Retina is an important part of human eyes, analyses of retinal vessels are preferable to the early prevention, detection and treatment of cardiovascular diseases. In this paper, we propose an effective unsupervised segmentation method. Firstly, the image is preprocessed to reduce interference of external factors; Secondly, the preprocessed image is enhanced to suppress the optic disc and background interference; Finally, based on the method of multiscale line detection, the blood vessels images are obtained. We evaluate our algorithm on the public datasets DRIVE and STARE, the experimental results show that the accuracy is about 95.90% and 96.11%, the sensitivity is 76.70% and 75.86%, the specificity is 95.20% and 95.56% respectively. Compared with other unsupervised methods, the proposed algorithm has a better segmentation effect.

A Robust Segmentation of Blood Vessels in Retinal Images Retinal blood vessels generally appear as wire mesh structures that have different widths. Their morphology plays a crucial role in indication and timely handling of fatal diseases, for instance diabetic retinopathy or hypertension. To address the aforementioned issues, this paper presents a robust vessel segmentation technique. First, the input retinal image is splitted into the Red, Green, and Blue Channels. On the Green channel preprocessing is done to eradicate noise or other disease. This step also extracts key feature, such as bright or dark contrast of the input retinal image through tophat morphological opening operation. Meanwhile, the Gaussian matched filters are applied along with different scales to achieve segmentation of thick or thin vessels. Finally, the pixelbypixel OR operation is applied to obtain final segmented image. Simulations on DRIVE and STARE datasets indicate that proposed technique surpasses recent methods in terms of Accuracy (Acc). The proposed technique is also computationally efficient than most of the recent methods compared therein.