April 2010
Volume 51, Issue 13
ARVO Annual Meeting Abstract  |   April 2010
Fundus Images Filtering by Principal Component Analysis
Author Affiliations & Notes
  • F. Moret
    Ophthalmology, University of Freiburg, Freiburg, Germany
  • W. A. Lagrèze
    Ophthalmology, University of Freiburg, Freiburg, Germany
  • M. Bach
    Ophthalmology, University of Freiburg, Freiburg, Germany
  • Footnotes
    Commercial Relationships  F. Moret, None; W.A. Lagrèze, None; M. Bach, None.
  • Footnotes
    Support  Deutsche Forschungsgemeinschaft BA 877/19-1
Investigative Ophthalmology & Visual Science April 2010, Vol.51, 1801. doi:
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      F. Moret, W. A. Lagrèze, M. Bach; Fundus Images Filtering by Principal Component Analysis. Invest. Ophthalmol. Vis. Sci. 2010;51(13):1801.

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      © ARVO (1962-2015); The Authors (2016-present)

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Purpose: : We propose a new filtering approach for canceling noise, laser speckles and vessels pulsatility in fundus images. The method is based on a Principal Component Analysis (PCA) and generates a single image free of dynamic components from a sequence of raw images.

Methods: : We collected sequences of 50 near-infrared images with a scanning laser ophthalmoscope (Spectralis, Heidelberg Engineering) in 5 healthy volunteers. The images were pre-processed (registration, micro-saccades rejection and artifacts masking) and PCA-filtered to extract the first Principal Component. Raw and PCA filtered images were then compared quantitatively and by medical experts.

Results: : We found an improved detectability of small features such as localized and faint hypo/hyper-pigmented areas and micro-vessels in PCA filtered images. In some subjects a clear relationship between both can be detected on PCA filtered images and not on raw images. We also identified specific artifacts caused by PCA filtering. One of them is a narrowing of the apparent diameter of the vessels and is due to the residual eye movements after registration and/or to local vessels pulsatility.

Conclusions: : Filtering of fundus images by PCA potentially improves diagnostic detection power, especially in retinal diseases which present only small or faint pathological features or which affect micro-vessels. The method can easily be incorporated in established imaging resources.

Keywords: image processing • imaging/image analysis: clinical • retina 

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