Ceylan, MuratYasar, Huseyin2020-03-262020-03-262013978-1-4799-0402-0; 978-1-4799-0403-7https://hdl.handle.net/20.500.12395/2927236th International Conference on Telecommunications and Signal Processing (TSP) -- JUL 02-04, 2013 -- Rome, ITALYRetinal imaging in ophthalmology plays an important role for the diagnosis of diabetes, cardiovascular disease, etc. In retina images, changes of blood vessels can help the expert to detection of diseases. Manually extraction of blood vessels from retinal images is usually difficult process due to depending on the experience of physician, back-ground artifacts, different acquisition process. Therefore, the aim of this study is to purpose a novel method for automatic blood vessel extraction from retinal image. This study presents a combined structure. This structure is realized with two cascade stages: feature extraction with 4th level Complex Wavelet Transform (CWT) and Complex-Valued Artificial Neural Networks (CVANN) for the blood vessels segmentation. To check the validation of proposed method, public DRIVE database is used. Result of this study has a higher accuracy (98.56 %) than previously studies in the literature.eninfo:eu-repo/semantics/closedAccessBlood vessel extractionretinal imagecomplex wavelet transformcomplex-valued artificial neural networkConference Object822825N/AWOS:000333968000166N/A