MODEL-BASED ILLUMINATION CORRECTION MODEL-BASED ILLUMINATION CORRECTION IN RETINAL IMAGESIN RETINAL IMAGES E. Grisan, A. Giani, E. Ceseracciu, A. RuggeriE. Grisan, A. Giani, E. Ceseracciu, A. Ruggeri

AbstractAbstract Retinal images are routinely acquired and assessed to provide diagnostic evidence for many important diseases. Because of the acquisition process, very often these images are non-uniformly illuminated and exhibit local luminosity and contrast variability. This problem may seriously affect the diagnostic process and its outcome, especially if an automatic computer-based procedure isused. We propose here a new method to estimate and correct luminosity variation in retinal images. The method uses the hue, saturation, value (HSV) colour space to better decouple the luminance and chromatic information. Then, it fits an illumination model on a proper subregion (the retinal background) of the saturation and value channels. This solves many of the drawbacks of previously proposed methods, as filter-based correction which fails when large lesions or retinal features are present.

IntroductionIntroductionRetinal images are acquired with a fundus camera, which records, on film or digital sensors such as CCD, the illumination light reflected by the retinal surface. Very often these images are unevenly or non-uniformly illuminated and thus local luminosity and contrast variability is present:

affect the diagnostic process and its outcome, since lesions in some areas may become hardly visible to a human observer.

images with large luminosity and contrast variability, both intra- and inter-image, are very difficult to analyze with such automatic systems and the obtained results may be of poor quality.

Fig. 1 Retinal images with varying illumination and different lesions

Fig. 2 Different regions of the same retinal image showing the same type of lesion (haemorrhage)

Normalization Procedure

High pass filteringLocally adaptive contrast enhancementLocally adaptive non-linear filtersMedian filter to extract slow variations of luminosity Non-linear point transformation for brightness adjustmentExploitation of vessel pixels luminosityBackground pixel estimation for luminosity and contrast estimation

Optic disc or Lesions visibility has to be preserved and hopefully improved by the normalization process,

approaches that estimate the correction locally fail in discriminating luminosity variations due to the presence of these features, from variations due to changes in illumination.

BUT

AcknowledgementsAcknowledgementsThis work was partly supported by a research grant from Nidek Technologies, Italy

BibliographyBibliography

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MethodsMethods

L(x,y)(x,y)IC(x,y)I(x,y) o

Aquired imageContrast distortion

factorLuminosity drift

),(ˆ),(ˆ),(

),(ˆyxC

yxLyxIyxIo

Double Paraboloid Model

Fig 3. Paraboloid model for theiInternal region

Fig 4. Paraboloid model

for theiperipheral region

Fig 5. Sigmoidal function to ensure smoothness on the combination of the paraboloids representing illumination in the interiorl and in the periphery of the image

Fig 6. Complete illumination model

Fig 7. From left to right, the original image, the estimated background pixels, end the estimated luminosity moddel on for the value channel

In retianl images two effects are usually present:a decrease of luminosity from the centre toward the peripheryaluminosity glare in the peripheryWe describe these effects by means of two elliptic paraboloid, whose parameters are estimated on different region of the fundus:the luminosity decrease in the interior part of the image, and the glare in the periphery. For physical reasons, we want the combination of the two models tobe continuous and to have a smooth transition between the regionsinfluenced by the two different illumination phenomena.

Interior regionPeripheral region

Fig 8. Retinal images with the luminosity corrected with the proposd method, and with mean and standard deviation imposed to be equal to those of the original images.

Results and discussionResults and discussionA new and effective algorithm to model the luminosity variation in fundus Images is proposed. This is based on the information contained in the retinal background pixels, on which a luminosity model is fit. This allows to cope with the presence of large lesions, that can be misinterpreted as luminosity variation by techniques relying onlyon local information. At variance with the methods available in the literature, the images with the proposed luminosity variability correction retain their chromatic appearance in every region of the image, and the large features and lesions present in the images are not affected by the correction: their borders remain well defined, and the difference between lesion and the surrounding retinal background is statistically conserved.By using a luminosity model, a physical motivated regularity is imposed on the shape of the luminosity, using information that global on the image. By this means, the methods is much more robust on the size of the blocks, or on the size of the kernels, used to estimate the retinal background or the luminosity variation, at the same time providing an accurate description of the luminosity variation.

Estimated background pixels (as in [8])