Noise-estimation-based anisotropic diffusion approach for retinal blood vessel segmentation
| العنوان: | Noise-estimation-based anisotropic diffusion approach for retinal blood vessel segmentation |
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| المؤلفون: | Mariem Ben Abdallah, Jihene Malek, Hichem Guedri, Ahmad Taher Azar, Hafedh Belmabrouk |
| المصدر: | Neural Computing and Applications. 29:159-180 |
| بيانات النشر: | Springer Science and Business Media LLC, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | Scale (ratio), Computer science, Anisotropic diffusion, Noise reduction, 02 engineering and technology, Fundus (eye), 030218 nuclear medicine & medical imaging, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Noise estimation, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, medicine, Segmentation, Computer vision, Retinal blood vessels, Retina, business.industry, Process (computing), Retinal, Diabetic retinopathy, medicine.disease, medicine.anatomical_structure, chemistry, 020201 artificial intelligence & image processing, Artificial intelligence, business, Software |
| الوصف: | 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. |
| تدمد: | 1433-3058 0941-0643 |
| DOI: | 10.1007/s00521-016-2811-9 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::acc87d32e9047e755b51fd9d75168eff https://doi.org/10.1007/s00521-016-2811-9 |
| Rights: | CLOSED |
| رقم الانضمام: | edsair.doi...........acc87d32e9047e755b51fd9d75168eff |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14333058 09410643 |
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| DOI: | 10.1007/s00521-016-2811-9 |