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Extraction Of Retinal Blood Vessels Using Curvelet Transform And Kirsch’s Templates

Retinal images play a major role in several applications including the ocular fundus operations and human recognition. They also play vital roles in detection of diabetes in early stages by comparing the states of retinal blood vessels. The detection of blood vessels from the retinal images is usually a tedious process. In this work a new algorithm to detect the blood vessels effectively has been proposed. The initial enhancement of the image is carried out using Adaptive Histogram Equalization, followed by this the curvelet Transforms are applied to the equalized image and the curvelet coefficients are obtained. The modifications to the Curvelet transform coefficients are carried out by suppressing all the coefficients of one band. The combined effect of the equalization and the Curvelet Transforms provides a better enhancement to the image. This enhanced image is used for the extraction of the blood vessels. The vessel extraction is done based on thresholding technique and the Kirsch’s templates. It involves spatial filtering of the image using the templates in eight different orientations. The masking of redundant regions in the obtained output image is carried out using boundary techniques. The salt and pepper noise in the image are removed by applying median filtering to the image. Experiments were done using a set of 20 images and have been proved that the algorithm holds good for all the images and blood vessels can be detected effectively.