TY - GEN
T1 - A GMM-based feature extraction technique for the automated diagnosis of Retinopathy of Prematurity
AU - Bolon-Canedo, V.
AU - Ataer-Cansizoglu, E.
AU - Erdogmus, D.
AU - Kalpathy-Cramer, J.
AU - Chiang, M. F.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/21
Y1 - 2015/7/21
N2 - Retinopathy of Prematurity (ROP) is an ophthalmic disease that is a leading cause of childhood blindness throughout the world. Accurate diagnosis of ROP is vital to identify infants who require treatment, which can prevent blindness. Arterial tortuosity and venous dilation in the retina are important signs of ROP, so it is necessary to extract these features from points on the vessels or vessel segments. Then, an image is represented with statistics such as minimum, maximum or mean of these values. However, these statistics provide biased estimates as an image contains both healthy and abnormal vessels. In this work, we present a novel feature extraction technique that represents each image with the parameters of a two-component Gaussian Mixture Model (GMM). Using these features, we performed classification experiments on a manually segmented retinal image dataset consisting of 77 images. The results show that GMM-based features outperform other features that are based on classical statistics, with accuracy over 90%. Moreover, if the features are extracted from the whole image without distinguishing veins and arteries, proposed features provide better performance compared to using traditional statistics.
AB - Retinopathy of Prematurity (ROP) is an ophthalmic disease that is a leading cause of childhood blindness throughout the world. Accurate diagnosis of ROP is vital to identify infants who require treatment, which can prevent blindness. Arterial tortuosity and venous dilation in the retina are important signs of ROP, so it is necessary to extract these features from points on the vessels or vessel segments. Then, an image is represented with statistics such as minimum, maximum or mean of these values. However, these statistics provide biased estimates as an image contains both healthy and abnormal vessels. In this work, we present a novel feature extraction technique that represents each image with the parameters of a two-component Gaussian Mixture Model (GMM). Using these features, we performed classification experiments on a manually segmented retinal image dataset consisting of 77 images. The results show that GMM-based features outperform other features that are based on classical statistics, with accuracy over 90%. Moreover, if the features are extracted from the whole image without distinguishing veins and arteries, proposed features provide better performance compared to using traditional statistics.
KW - Retinopathy of prematurity
KW - classification
KW - feature extraction
UR - http://www.scopus.com/inward/record.url?scp=84944321422&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944321422&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2015.7164161
DO - 10.1109/ISBI.2015.7164161
M3 - Conference contribution
AN - SCOPUS:84944321422
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1498
EP - 1501
BT - 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PB - IEEE Computer Society
T2 - 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
Y2 - 16 April 2015 through 19 April 2015
ER -