Paper Info
Title
Automatic detection of retina disease: robustness to image quality and localization of anatomy structure.
Abstract
The automated detection of diabetic retinopathy and other eye diseases in images of the retina has great promise as a low-cost method for broad-based screening. Many systems in the literature which perform automated detection include a quality estimation step and physiological feature detection, including the vascular tree and the optic nerve / macula location. In this work, we study the robustness of an automated disease detection method with respect to the accuracy of the optic nerve location and the quality of the images obtained as judged by a quality estimation algorithm. The detection algorithm features microaneurysm and exudate detection followed by feature extraction on the detected population to describe the overall retina image. Labeled images of retinas ground-truthed to disease states are used to train a supervised learning algorithm to identify the disease state of the retina image and exam set. Under the restrictions of high confidence optic nerve detections and good quality imagery, the system achieves a sensitivity and specificity of 94.8% and 78.7% with area-under-curve of 95.3%. Analysis of the effect of constraining quality and the distinction between mild non-proliferative diabetic retinopathy, normal retina images, and more severe disease states is included.
Year
DOI
Venue
2011
10.1109/IEMBS.2011.6091473
EMBC
Keywords
Field
DocType
eye,macula location,robustness,mild nonproliferative diabetic retinopathy,quality estimation algorithm,eye disease,image quality,diseases,learning (artificial intelligence),disease states,automatic detection,optic nerve detection,feature extraction,physiological feature detection,retina disease,retina image,microaneurysm,optic nerve location,anatomy structure,vascular tree,medical image processing,supervised learning algorithm,anatomy,image processing,optical filters,adaptive optics,ground truth,area under curve,learning artificial intelligence,supervised learning,optical imaging,feature detection
Diabetic retinopathy,Population,Computer vision,Computer science,Retina,Image quality,Image processing,Feature extraction,Robustness (computer science),Artificial intelligence,Optic nerve
Conference
Volume
ISSN
ISBN
2011
1557-170X
978-1-4244-4122-8
Citations 
PageRank 
References 
2
0.43
6
Authors
7
Name
Order
Citations
PageRank
T P Karnowski130.88
D Aykac220.43
Luca Giancardo314112.52
Yan Liu419730.85
Thomas E. Nichols52802282.80
Kenneth W. Tobin622518.78
Edward Chaum722414.72