Turkish Journal of Computer and Mathematics Education Vol.12 No.9 (2021), 370-382 370 Research Article A Novel Multi-Orientation Kernel for Retina Vessel Detection M. VijayaMaheswari a and G. Murugeswari b , a,b Department of Computer Science and Engineering,ManonmaniamSundaranar University,Tirunelveli, Tamil Nadu Article History: Received: 10 January 2021; Revised: 12 February 2021; Accepted: 27 March 2021; Published online: 20 April 2021 Abstract: Retina is a thinnest tissue comprises of millions of blood vessels. It plays a vital role in the human eye that carries the visual signals to the brain for the interpretation. Any damage to the blood vessels in the retina causes serious issues related to the vision and it leads to the chronicle eye diseases like glaucoma, macular degeneration, diabetic retinopathy etc. Diabetic Retinopathy (DR) is a threatening disease among the diabetic patients in the recent years. Damage to the blood vessels causes DR. As the number of diabetic patients are comparatively high these days, it has become mandatory for the development of accurate system for segmenting the blood vessels in the retina which will reduce the work load of ophthalmologists to a greater extent. In this work we propose a novel technique named Multi-Orientation Kernel (MOK) for blood vessel detection. We also propose a framework for segmentation of retina blood vessels which follows the sequence of steps such as preprocessing, blood vessel extraction using proposed kernel (MOK) and refinement usingActive Contour method. This proposed method is tested on DRIVE and CHASE_DB1 dataset. The proposed algorithm produced 95% of accuracy on DRIVE datasetand 96% of accuracy on CHASE_DB1 dataset respectively. The performance of proposed approach is compared with few existing techniques. Keywords: Kernel, retina, Blood vessels, Segmentation, Active Contour, Detection. 1.Introduction Human eye is made up of millions of arteries and veins. Blood vessels in retina consists of multiple branches that carries blood to all parts of the eye. These vessels in the retina look like a thin hair like structure. Segmentation of blood vessels plays a significant role which helps ophthalmologists for identifying different types of eye diseases like cataracts, macular edema, glaucoma, diabetic retinopathy etc. Each retinal disease has different symptoms and disorder ranging from mild defects to severe defects causing blindness. Because of the increase of diabetic patients day by day, diabetic retinopathy has become a vision threatening disease among the patients with diabetic retinopathy and therefore the demand for the automated system has also increased which reduces the workload of the physicians. When there is a mild defect, it is necessary for the eyes to be checked and take treatment. In mild defect, the conditions may benign, but in severe cases it may lead to serious issues. Early detection and treatment is important for diabetic retinopathy. In the earlier stage, laser treatment can be given to patients to avoid the damage to the blood vessels, because the advanced stage of this disease causes total blindness. Manual screening is always a difficult and challenging tasks for the ophthalmologists in diagnosing eye diseases. The type of an image acquired as well as the quality of an image, also decide the accuracy of blood vessel segmentation. Features like length of the vessel, reddish spots on the vessels and branches of blood vessels are extremely difficult to segment manually. Before applying the segmentation techniques most of the images require preprocessing step which enhances the quality of the image thereby improving the accuracy of segmentation. 1.1 Motivations A study suggests [1], 285 million people have visual defect globally. In the year of 2020, there were about 12,000 ophthalmologists for 135 billion people, in India. It is observed that there exists one ophthalmologist for 90,000 people. There is a higher demand for ophthalmologists throughout globally. It is expected to be 25,000 but still our Nation has only 12,000 specialists to treat eye deformities. Manual screening is always a time consuming process. Computer Aided Diagnosis (CAD) system reduces the amount of workload of ophthalmologist in diagnosing the retinal disease. Motivated by this fact, we propose a novel Multi-Orientation Kernel for retina vessel detection and a framework for vessel segmentation which can also be used for CAD system development. 1.2Issues and Challenges in Retinal Image Analysis • The major issue in retinal image analysis is the quality of the image captured. Some cameras are subject to significant amount of noise which are used for acquiring the retina images. The quality of the camera is taken into account. The captured image must be of high quality for image analysis. Noise may be produced if the camera is not a quality one. • Another important issue in retinal image is about the movement of eye ball of a patient while capturing the image. During funduscopic examination some patients are not able to co-operate due to anxiety. Because of the abnormal movement of eye ball and non-cooperation of the patients, ophthalmologist might acquire a blurred image of retina.