Segmenting the eye fundus images for identification of blood vessels

Retinal (eye fundus) images are widely used for diagnostic purposes by ophthalmologists. The normal features of eye fundus images include the optic nerve disc, fovea and blood vessels. Algorithms for identifying blood vessels in the eye fundus image generally fall into two classes: extraction of vessel information and segmentation of vessel pixels. Algorithms of the rst group start on known vessel point and trace the vasculature structure in the image. Algorithms of the second group perform a binary classi cation (vessel or non-vessel, i.e. background) in accor- dance of some threshold. We focus here on the binarization [4] methods that adapt the threshold value on each pixel to the global/local image characteristics. Global binarization methods [5] try to nd a single threshold value for the whole image. Local binarization methods [3] compute thresholds individually for each pixel using information from the local neighborhood of the pixel. In this paper, we modify and improve the Sauvola local binarization method [3] by extending its abilities to be applied for eye fundus pictures analysis. This method has been adopted for auto- matic detection of blood vessels in retinal images. We suggest automatic parameter selection for Sauvola method. Our modi cation allows determine/extract the blood vessels almost independently of the brightness of the picture