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

In this study, an unsupervised retina blood vessel segmentation process was performed on the gray level images with principal component analysis (PCA) and the green channel of the RGB image, which most clearly shows retinal vessels and the results were compared. The images were improved for a good segmentation by using contrast-limited adaptive histogram equalization (CLAHE), color intensity adjustment, morphological operations and median and Gaussian filtering. Retinal vessel structures were segmented with top-hat and bot-hat morphological operations and converted to binary image by using global thresholding. 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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