Branch Mathematics and Statistics Faculty and Staff Publications
Document Type
Article
Publication Date
10-2017
Abstract
A fundus image is an effective tool for ophthalmologists studying eye diseases. Retinal vessel detection is a significant task in the identification of retinal disease regions. This study presents a retinal vessel detection approach using shearlet transform and indeterminacy filtering. The fundus image’s green channel is mapped in the neutrosophic domain via shearlet transform. The neutrosophic domain images are then filtered with an indeterminacy filter to reduce the indeterminacy information. A neural network classifier is employed to identify the pixels whose inputs are the features in neutrosophic images. The proposed approach is tested on two datasets, and a receiver operating characteristic curve and the area under the curve are employed to evaluate experimental results quantitatively. The area under the curve values are 0.9476 and 0.9469 for each dataset respectively, and 0.9439 for both datasets. The comparison with the other algorithms also illustrates that the proposed method yields the highest evaluation measurement value and demonstrates the efficiency and accuracy of the proposed method.
Publication Title
Symmetry
Volume
9
First Page
235
DOI
doi:10.3390/sym9100235
Language (ISO)
English
Keywords
retinal vessels detection; shearlet transform; neutrosophic set; indeterminacy filtering; neural network; fundus image
Recommended Citation
Smarandache, Florentin; Yanhui Guo; Umit Budak; and Abdulkadir Sengur.
"A Retinal Vessel Detection Approach Based on Shearlet Transform and Indeterminacy Filtering on Fundus Images."
Symmetry
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Included in
Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, Health Information Technology Commons, Mathematics Commons
