A GMM-based feature extraction technique for the automated diagnosis of Retinopathy of Prematurity

V. Bolon-Canedo, E. Ataer-Cansizoglu, D. Erdogmus, J. Kalpathy-Cramer, M. F. Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PublisherIEEE Computer Society
Pages1498-1501
Number of pages4
ISBN (Electronic)9781479923748
DOIs
StatePublished - Jul 21 2015
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: Apr 16 2015Apr 19 2015

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2015-July
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
CountryUnited States
CityBrooklyn
Period4/16/154/19/15

Keywords

  • Retinopathy of prematurity
  • classification
  • feature extraction

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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    Bolon-Canedo, V., Ataer-Cansizoglu, E., Erdogmus, D., Kalpathy-Cramer, J., & Chiang, M. F. (2015). A GMM-based feature extraction technique for the automated diagnosis of Retinopathy of Prematurity. In 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015 (pp. 1498-1501). [7164161] (Proceedings - International Symposium on Biomedical Imaging; Vol. 2015-July). IEEE Computer Society. https://doi.org/10.1109/ISBI.2015.7164161