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

Esra Kaya, Ismail Saritas, Murat Ceylan
  • Murat Ceylan
    Selcuk University, Turkey


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.


Blood Vessel; Unsupervised Learning; Retina; Segmentation

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Submitted: 2017-06-16 11:20:42
Published: 2017-12-12 13:20:45
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