A New Seeded Region Growing Technique for Retinal Blood Vessels Extraction

Distribution of retinal blood vessels (RBVs) in retinal images has an important role in the prevention, diagnosis, monitoring and treatment of diseases, such as diabetes, high blood pressure, or heart disease. Therefore, detection of the exact location of RBVs is very important for Ophthalmologists. One of the frequently used techniques for extraction of these vessels is region growing-based Segmentation. In this paper, we propose a new region growing (RG) technique for RBVs extraction, called cellular automata-based segmentation. RG techniques often require manually seed point selection, that is, human intervention. However, due to the complex structure of vessels in retinal images, manual tracking of them is very difficult. Therefore, to make our proposed technique full automatic, we use an automatic seed point selection method. The proposed RG technique was tested on Digital Retinal Images for Vessel Extraction database for three different initial seed sets and evaluated against the manual segmentation of retinal images available at this database. Three quantitative criteria including accuracy, true positive rate and false positive rate, were considered to evaluate this method. The visual scrutiny of the segmentation results and the quantitative criteria show that, using cellular automata for extracting the blood vessels is promising. However, the important point at here is that the correct initial seeds have an effective role on the final results of segmentation.