Blood Vessel Segmentation from Retina Images Using Curvelet Transform and Multi-Structure Elements Morphology

Retinal images play vital role in several applications such as disease diagnosis and human recognition. They also play a major role in early detection of diabetics by comparing the states of the retinal blood vessels. The detection of blood vessels from the retinal images is a tedious process. In this work a new algorithm to detect the blood vessels effectively has been proposed. Initially enhancement of the image is carried out using curvelet transform and modification of the curvelet coefficients. Since the blood vessels are distributed in various directions, morphology processing with multidirectional structuring elements are used to extract the blood vessel from the retinal images. Afterwards, morphological operator by reconstruction using multistructure elements eliminates the ridges not belonging to the vessel tree. A simple thresholding along with connected component analysis (CCA) indicates the remained ridges belonging to vessel tree. Finally applying length filter on the connected components all residual ridges are refined from the images. Experimental results show that the blood vessels are extracted from the retinal images with better PSNR and 96% accuracy than enhancement using other techniques INTRODUCTION One of the most important internal components in eye is called retina, covering all posterior compartment, on which all optic receptors are distributed. Disorders in retina resulted from special diseases are diagnosed by special images from retina, which are obtained by using optic imaging called fundus. Blood vessel is one of the most important features in retina consisting of arteries and arterioles for detecting retinal vein occlusion, grading the tortuosity for hypertension and early diagnosis of glaucoma . Checking the obtained changes in retinal images in an especial period can help the physician to diagnose the disease. Applications of retinal images are diagnosing the progress of some cardiovascular diseases, diagnosing the region with no blood vessels (Macula), using such images in helping automatic laser surgery on eye, and using such images in biometric applications, etc. On the other hand, extracting the retinal blood vessels is done in some cases by physician manually, which is difficult and time consuming and is accompanied by high mistakes due to much dependence on the physician’s skill level. So, the exact extraction of the blood vessels from the retinal images necessitates using algorithm and instruments which reduce the dependency on the functor and eliminate the error factors.