Unsupervised change-detection in retinal images by a multiple-classifier approach

  • Authors:

  • Giulia Troglio;Marina Alberti;Jón Atli Benediksson;Gabriele Moser;Sebastiano Bruno Serpico;Einar Stefánsson

  • Affiliations:

  • Dept. of Biophysical and Electronic Eng. (DIBE), University of Genoa, Genoa, Italy;Dept. of Biophysical and Electronic Eng. (DIBE), University of Genoa, Genoa, Italy;Faculty of Electrical and Computer Eng. and Dept. of Ophthalmology, University of Iceland, Reykjavik, Iceland;Dept. of Biophysical and Electronic Eng. (DIBE), University of Genoa, Genoa, Italy;Dept. of Biophysical and Electronic Eng. (DIBE), University of Genoa, Genoa, Italy;Faculty of Electrical and Computer Eng. and Dept. of Ophthalmology, University of Iceland, Reykjavik, Iceland

  • Venue:
  • MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
  • Year:
  • 2010

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Abstract

The aim of this work is the development of an unsupervised method for the detection of the changes that occurred in multitemporal digital images of the fundus of the human retina, in terms of white and red spots. The images are acquired from the same patient at different times by a fundus camera. The proposed method is an unsupervised multiple classifier approach, based on a minimum-error thresholding technique. This technique is applied to separate the “change” and the “no-change” areas in a suitably defined difference image. In particular, the thresholding approach is applied to selected sub-images: the outputs of the different windows are combined with a majority vote approach, in order to cope with local illumination differences. A quantitative assessment of the change detection performances suggests that the proposed method is able to provide accurate change maps, although possibly affected by misregistration errors or calibration/acquisition artifacts. The comparison between the results obtained using the implemented multiple classifier approach and a standard one points out that the proposed algorithm provides an accurate detection of the temporal changes.