Illumination normalization of retinal images using sampling and interpolation

Blood vessels in retinal images are often spread wildly across the image surface. By using this feature, this paper presents a novel approach for illumination normalization of retinal images. With the assumption that the reflectance of the vessels (including both major and small vessels) is a constant, it was found in our study that the illumination distribution of a retinal image can be estimated based on the locations of the vessel pixels and their intensity values. The procedures for estimating the illumination consists of two steps: (1) obtain the vessel map of the retinal image, and (2) estimate the illumination function (IF) of the image by interpolating the intensity values (luminance) of non-vessel pixels using a bicubic model function based on the locations of the vessel pixels and their intensity values. The illumination-normalized image can then be obtained by subtracting the original image from the estimated IF.20 non-uniformly illuminated sample retinal images that were tested using the proposed method. The results showed that the over-all standard deviation of the illumination for the image background reduced by 56.8% from 19.82 to 8.56, and the signal-to-noise ratio of the normalized images was greatly improved in the application of the global thresholding for image/region segmentation. Furthermore, when measured by the local luminosity histograms, the contrast of regions with low illumination containing features that are normally difficult to detect (such as small lesions and vessels) was also enhanced significantly. Therefore, it is concluded that the proposed method can be used to produce a desirable illumination- normalized image, from which region segmentation can be made easier and more accurate.