Omkar2118/datasets huggingface dataset:
DRIVE
Full name: Digital Retinal Images for Vessel Extraction

The **Digital Retinal Images for Vessel Extraction** (**DRIVE**) dataset is a dataset for retinal vessel segmentation. It consists of a total of JPEG 40 color fundus images; including 7 abnormal pathology cases. The images were obtained from a diabetic retinopathy screening program in the Netherlands. The images were acquired using Canon CR5 non-mydriatic 3CCD camera with FOV equals to 45 degrees. Each image resolution is 584*565 pixels with eight bits per color channel (3 channels). 

The set of 40 images was equally divided into 20 images for the training set and 20 images for the testing set. Inside both sets, for each image, there is circular field of view (FOV) mask of diameter that is approximately 540 pixels. Inside training set, for each image, one manual segmentation by an ophthalmological expert has been applied. Inside testing set, for each image, two manual segmentations have been applied by two different observers, where the first observer segmentation is accepted as the ground-truth for performance evaluation.

Source: [Ant Colony based Feature Selection Heuristics for Retinal Vessel Segmentation](https://arxiv.org/abs/1403.1735)
Image Source: [https://drive.grand-challenge.org/](https://drive.grand-challenge.org/)

Introducing paper: Ridge-based vessel segmentation in color images of the retina