You are in:Home/Publications/Ben Abdallah M, Azar AT, Guedri H, Malek J, Belmabrouk H (2018) Noise-estimation-based anisotropic diffusion approach for retinal blood vessel segmentation. Neural Computing and Applications, 29(8): 159–180. [ISI Indexed: Impact Factor: 4.215].

Dr. Assoc. Prof. Ahmad Taher Azar :: Publications:

Title:
Ben Abdallah M, Azar AT, Guedri H, Malek J, Belmabrouk H (2018) Noise-estimation-based anisotropic diffusion approach for retinal blood vessel segmentation. Neural Computing and Applications, 29(8): 159–180. [ISI Indexed: Impact Factor: 4.215].
Authors: Not Available
Year: 2018
Keywords: Not Available
Journal: Neural Computing and Applications
Volume: 29
Issue: 8
Pages: 159–180
Publisher: Springer
Local/International: International
Paper Link:
Full paper Not Available
Supplementary materials Not Available
Abstract:

Recently, numerous research works in retinal-structure analysis have been performed to analyze retinal images for diagnosing and preventing ocular diseases such as diabetic retinopathy, which is the first most common causes of vision loss in the world. In this paper, an algorithm for vessel detection in fundus images is employed. First, a denoising process using the noise-estimation-based anisotropic diffusion technique is applied to restore connected vessel lines in a retinal image and eliminate noisy lines. Next, a multi-scale line-tracking algorithm is implemented to detect all the blood vessels having similar dimensions at a selected scale. An openly available dataset, called “the STARE Project’s dataset,” has been firstly utilized to evaluate the accuracy of the proposed method. Accordingly, our experimental results, performed on the STARE dataset, depict a maximum average accuracy of around 93.88%. Then, an experimental evaluation on another dataset, named DRIVE database, demonstrates a satisfactory performance of the proposed technique, where the maximum average accuracy rate of 93.89% is achieved.

Google ScholarAcdemia.eduResearch GateLinkedinFacebookTwitterGoogle PlusYoutubeWordpressInstagramMendeleyZoteroEvernoteORCIDScopus