Detection of retinal changes from illumination normalized fundus images using convolutional neural networks

Kedir M. Adal; Peter G. van Etten; Jose P. Martinez; Kenneth Rouwen; Koenraad A. Vermeer; Lucas J. van Vliet

doi:10.1117/12.2254342

3 March 2017 Detection of retinal changes from illumination normalized fundus images using convolutional neural networks

Kedir M. Adal, Peter G. van Etten, Jose P. Martinez, Kenneth Rouwen, Koenraad A. Vermeer, Lucas J. van Vliet

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101341N (2017) https://doi.org/10.1117/12.2254342
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Automated detection and quantification of spatio-temporal retinal changes is an important step to objectively assess disease progression and treatment effects for dynamic retinal diseases such as diabetic retinopathy (DR). However, detecting retinal changes caused by early DR lesions such as microaneurysms and dot hemorrhages from longitudinal pairs of fundus images is challenging due to intra and inter-image illumination variation between fundus images. This paper explores a method for automated detection of retinal changes from illumination normalized fundus images using a deep convolutional neural network (CNN), and compares its performance with two other CNNs trained separately on color and green channel fundus images. Illumination variation was addressed by correcting for the variability in the luminosity and contrast estimated from a large scale retinal regions. The CNN models were trained and evaluated on image patches extracted from a registered fundus image set collected from 51 diabetic eyes that were screened at two different time-points. The results show that using normalized images yield better performance than color and green channel images, suggesting that illumination normalization greatly facilitates CNNs to quickly and correctly learn distinctive local image features of DR related retinal changes.

Citation Download Citation

Kedir M. Adal, Peter G. van Etten, Jose P. Martinez, Kenneth Rouwen, Koenraad A. Vermeer, and Lucas J. van Vliet "Detection of retinal changes from illumination normalized fundus images using convolutional neural networks", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101341N (3 March 2017); https://doi.org/10.1117/12.2254342

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available