Cataract Retinal Image Quality Assessment using Deep Learning for Glaucoma Diagnosis

Glaucoma is a group of eye diseases that gradually leads to peripheral vision loss and blindness. It is affecting about 90 million people worldwide and usually painless.
Glaucoma has no cure and it advances moderately if not treated on time. Therefore early and fast diagnosis is crucial for effective treatment. 

In this work I examine to what extent inferior retinal images affects glaucoma diagnosis. Then I develop a Retinal Image Quality Assessment (RIQA) system accordingly,
to screen out irrelevant images for diagnosis. For this purpose cataract hazed images are chosen, since cataract is the main blindness cause in the western world and is highly
common in older ages. Cataract simulated images are created using the Dark Channel Prior (DCP) technique. 

For the second step I perform dehazing algorithm on the cataract retinal images and test diagnosis results again, to assess the dehazing algorithm. I conclude at what cataract
level glaucoma diagnosis is just unreliable, even after attempts to improve image quality by pre-processing.  

Matar Tzur (Buchbinder) is MSc student at the Technion under the supervision of Prof. Moshe Porat and Dr. Zvi Friedman. Matar received her BSc in Electrical Engineer

from the Technion. Her research interests include Machine and deep learning methods and their applications in computer vision and image processing.