Retinal Blood Vessels Segmentation Using the Curvlet Transform

Abstract

Retinal image having very vital information. It plays important roles in finding of some diseases in early stages, such as diabetes, and cardiovascular disease. In this proposed system a new algorithm used to detect the blood vessels effectively from the retinal image. The initial image enhancement is carried out by using Adaptive Histogram Equalization, followed by 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. This 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 blood vessels. Afterward, eliminate the ridges not belonging to the vessels tree by morphological operators by reconstruction while trying to preserve the thin vessels unchanged. In order to increase the efficiency of the morphological operators by reconstruction, they were applied using multi-structure elements and local adaptive thresholding method along with connected components analysis (CCA) indicates the remained ridges belonging to vessels.