Comparison of Unsupervised Segmentation of Retinal Blood Vessels in Gray Level Image with PCA and Green Channel Image

Authors

DOI:

https://doi.org/10.18201/ijisae.2017533857

Keywords:

Blood Vessel, Unsupervised Learning, Retina, Segmentation

Abstract

In this study, an unsupervised retina blood vessel segmentation process was performed on the gray level images with PCA and the green channel of the RGB image, which most clearly shows retinal vessels and the results were compared. The average accuracy rate obtained for the gray level image with PCA after the study was 0,9443, while the average accuracy rate obtained for the green channel was 0,9685. The study was performed using 40 images in the DRIVE data set which is one of the most common retina data sets known.

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Author Biographies

Esra Kaya, Selcuk University

Research Assistant at Electrical and Electronics Engineering Department, Faculty of Technology, Selcuk University

Ismail Saritas, Selcuk University

Assoc. Prof. Dr. at Electrical and Electronics Engineering Department, Faculty of Technology, Selcuk University

Murat Ceylan, Selcuk University

Assist. Prof. Dr. at Electrical and Electronics Engineering Department, Faculty of Engineering, Selcuk University

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Published

12.12.2017

How to Cite

Kaya, E., Saritas, I., & Ceylan, M. (2017). Comparison of Unsupervised Segmentation of Retinal Blood Vessels in Gray Level Image with PCA and Green Channel Image. International Journal of Intelligent Systems and Applications in Engineering, 5(4), 163–167. https://doi.org/10.18201/ijisae.2017533857

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Research Article

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