Classifying glaucoma with image-based features from fundus photographs

Rüdiger Bock, Jörg Meier, Georg Michelson, László G. Nyúl, Joachim Hornegger

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

58 Citations (Scopus)

Abstract

Glaucoma is one of the most common causes of blindness and it is becoming even more important considering the ageing society. Because healing of died retinal nerve fibers is not possible early detection and prevention is essential. Robust, automated mass-screening will help to extend the symptom-free life of affected patients. We devised a novel, automated, appearance based glaucoma classification system that does not depend on segmentation based measurements. Our purely data-driven approach is applicable in large-scale screening examinations. It applies a standard pattern recognition pipeline with a 2-stage classification step. Several types of image-based features were analyzed and are combined to capture glaucomatous structures. Certain disease independent variations such as illumination inhomogeneities, size differences, and vessel structures are eliminated in the preprocessing phase. The "vesselfree" images and intermediate results of the methods are novel representations of the data for the physicians that may provide new insight into and help to better understand glaucoma. Our system achieves 86 % success rate on a data set containing a mixture of 200 real images of healthy and glaucomatous eyes. The performance of the system is comparable to human medical experts in detecting glaucomatous retina fundus images.

Original languageEnglish
Title of host publicationPattern Recognition - 29th DAGM Symposium, Proceedings
PublisherSpringer Verlag
Pages355-364
Number of pages10
ISBN (Print)3540749330, 9783540749332
DOIs
Publication statusPublished - 2007
Event29th Annual Symposium of the Deutsche Arbeitsgemeinschaft fur Mustererkennung, DAGM 2007 - Heidelberg, Germany
Duration: Sep 12 2007Sep 14 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4713 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other29th Annual Symposium of the Deutsche Arbeitsgemeinschaft fur Mustererkennung, DAGM 2007
CountryGermany
CityHeidelberg
Period9/12/079/14/07

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Bock, R., Meier, J., Michelson, G., Nyúl, L. G., & Hornegger, J. (2007). Classifying glaucoma with image-based features from fundus photographs. In Pattern Recognition - 29th DAGM Symposium, Proceedings (pp. 355-364). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4713 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-74936-3_36