A Simple U-net model for Retinal Blood Vessel Segmentation based on tensorflow2

Project for segmentation of blood vessels, microaneurysm and hardexudates in fundus images.

exudates detection using hybrid approach (Image Morphology & Machine Learning)

Support repository for the paper "Retinal vessel segmentation based on Fully Convolutional Neural Networks", Expert Systems with Applications, Volume 112, 1 December 2018, Pages 229-242.

Implementing a remote sensing object detector using Tensorflow object detection API

The Hamilton Eye Institute Macular Edema Dataset (HEI-MED) (formerly DMED) is a collection of 169 fundus images to train and test image processing algorithms for the detection of exudates and diabetic macular edema. The images have been collected as part of a telemedicine network for the diagnosis of diabetic retinopathy

Optic disc detection in a retina image using a fully connected convolutional neural network.

Diabetic classification based on retinal images

A deep learning model built to detect cataract in human eyes using the VGG-19 pretrained weights

MOVED to https://gitlab.com/nicstrisc/B-COSFIRE-MATLAB. Matlab implementation of the B-COSFIRE filters for detection of curvilinear patterns in images

AI telegram-bot - "MedEyeService"

Retina Imaging Toolbox (RIT)

Voronoi analysis of vascular networks in the eye

A tool for quantitative analysis of sprouting angiogenesis and lumen space

(CS F266) An ImageJ macro for Quantitative Analysis of OCTA images.

Providing retinal image analysis through your smartphone.

Material related to "Caracterización de la morfología foveal: parametrización, diferencias de sexo y efectos de la edad", CASEIB, 2020