Images of the eye ground not only provide an insight to important parts of the visual system but also reflect the general state of health of the entire human body. Automatic retina image analysis is becoming an important screening tool for early detection of certain risks and diseases. Glaucoma is one of the most common causes of blindness and is becoming even more important considering the ageing society. Robust mass-screening may help to extend the symptom-free life of affected patients. Our research is focused on a novel automated classification system for glaucoma, based on image features from fundus photographs. Our new data-driven approach requires no manual assistance and does not depend on explicit structure segmentation and measurements. First, disease independent variations, such as nonuniform illumination, size differences, and blood vessels are eliminated from the images. Then, the extracted high-dimensional feature vectors are compressed via PCA and combined before classification with SVMs takes place. The technique achieves an accuracy of detecting glaucomatous retina fundus images comparable to that of human experts. The "vessel-free" images and intermediate output of the methods are novel representations of the data for the physicians that may provide new insight into and help to better understand glaucoma.

Date Published: 30 October 2009
PDF: 9 pages
Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 74971C (30 October 2009); doi: 10.1117/12.851179

Published in SPIE Proceedings Vol. 7497:
MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques
Faxiong Zhang; Faxiong Zhang, Editor(s)