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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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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.
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.
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.
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.
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