MODEL-BASED ILLUMINATION CORRECTION IN RETINAL IMAGES

1. MODEL-BASED ILLUMINATION CORRECTION IN RETINAL IMAGES E. Grisan, A. Giani, E. Ceseracciu, A. Ruggeri Abstract 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 is used. 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. Introduction Results and discussion Interior region Peripheral region Optic disc or Lesions visibility has to be preserved and hopefully improved by the normalization process, A 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 only on 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. • Retinal 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. BUT 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. Methods Contrast distortion factor Aquired image Luminosity drift Fig 4. Paraboloid model for theiperipheral region Fig 3. Paraboloid model for theiInternal region Fig. 1 Retinal images with varying illumination and different lesions 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. • In retianl images two effects are usually present: • a decrease of luminosity from the centre toward the periphery • aluminosity glare in the periphery • We 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 to • be continuous and to have a smooth transition between the regions • influenced by the two different illumination phenomena. 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 Acknowledgements This work was partly supported by a research grant from Nidek Technologies, Italy Bibliography [1] B. M. Ege, O. K. Hejlesen, O. V. Larsen, K. Møller,B. Jennings, D. Kerr, and D. A. Cavan, “Screening for diabetic retinopathy using computer based image analysis and statistical classification,” Computer Methods and Programs in Biomedicine, vol. 62, pp. 165–175, 2000. [2] R. C. Gonzalez and R. E.Woods, Digital Image Processing, Addison Wesley Publishing Company, 1992. [3] R. Wallis, “An approach to the space variant restoration and enhancement of images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Naval Postgraduate School, Monterey (CA), USA, 1976. [4] G. E. Øien and P. 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Fig. 2 Different regions of the same retinal image showing the same type of lesion (haemorrhage) Estimated background pixels (as in [8]) Double Paraboloid Model Fig 6. Complete illumination model Normalization Procedure • High pass filtering • Locally adaptive contrast enhancement • Locally adaptive non-linear filters • Median filter to extract slow variations of luminosity • Non-linear point transformation for brightness adjustment • Exploitation of vessel pixels luminosity • Background pixel estimation for luminosity and contrast estimation Fig 7. From left to right, the original image, the estimated background pixels, end the estimated luminosity moddel on for the value channel