bstract — 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.