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

Full Text:

Submitted: 2017-06-16 11:20:42
Published: 2017-12-12 13:20:45
Search for citations in Google Scholar
Related articles: Google Scholar


M. Ceylan and H. YAŞAR, "A novel approach for automatic blood vessel extraction in retinal images: complex ripplet-I transform and complex valued artificial neural network," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 24, no. 4, pp. 3212-3227, 2016.

N. P. Singh and R. Srivastava, "Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter," Computer methods and programs in biomedicine, vol. 129, pp. 40-50, 2016.

S. Aslani and H. Sarnel, "A new supervised retinal vessel segmentation method based on robust hybrid features," Biomedical Signal Processing and Control, vol. 30, pp. 1-12, 2016.

G. Hassan, N. El-Bendary, A. E. Hassanien, A. Fahmy, and V. Snasel, "Retinal blood vessel segmentation approach based on mathematical morphology," Procedia Computer Science, vol. 65, pp. 612-622, 2015.

E. Imani, M. Javidi, and H.-R. Pourreza, "Improvement of retinal blood vessel detection using morphological component analysis," Computer methods and programs in biomedicine, vol. 118, no. 3, pp. 263-279, 2015.

M. Frucci, D. Riccio, G. S. di Baja, and L. Serino, "Severe: Segmenting vessels in retina images," Pattern Recognition Letters, vol. 82, pp. 162-169, 2016.

G. Kovács and A. Hajdu, "A self-calibrating approach for the segmentation of retinal vessels by template matching and contour reconstruction," Medical image analysis, vol. 29, pp. 24-46, 2016.

J. V. Soares, J. J. Leandro, R. M. Cesar, H. F. Jelinek, and M. J. Cree, "Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification," IEEE Transactions on medical Imaging, vol. 25, no. 9, pp. 1214-1222, 2006.

M. Ceylan and H. Yacar, "Blood vessel extraction from retinal images using complex wavelet transform and complex-valued artificial neural network," in Telecommunications and Signal Processing (TSP), 2013 36th International Conference on, 2013, pp. 822-825: IEEE.

R. Vega, G. Sanchez-Ante, L. E. Falcon-Morales, H. Sossa, and E. Guevara, "Retinal vessel extraction using lattice neural networks with dendritic processing," Computers in biology and medicine, vol. 58, pp. 20-30, 2015.

S. Wang, Y. Yin, G. Cao, B. Wei, Y. Zheng, and G. Yang, "Hierarchical retinal blood vessel segmentation based on feature and ensemble learning," Neurocomputing, vol. 149, pp. 708-717, 2015.

J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B. v. Ginneken, "Ridge-based vessel segmentation in color images of the retina," IEEE Transactions on Medical Imaging, vol. 23, no. 4, pp. 501-509, 2004.

C. Zhu et al., "Retinal vessel segmentation in colour fundus images using Extreme Learning Machine," Computerized Medical Imaging and Graphics, vol. 55, pp. 68-77, 2017.

A. A. Gooch, S. C. Olsen, J. Tumblin, and B. Gooch, "Color2Gray: salience-preserving color removal," ACM Trans. Graph., vol. 24, no. 3, pp. 634-639, 2005.

K. Rasche, R. Geist, and J. Westall, "Re‐coloring Images for Gamuts of Lower Dimension," in Computer Graphics Forum, 2005, vol. 24, no. 3, pp. 423-432: Wiley Online Library.

G. R. Kuhn, M. M. Oliveira, and L. A. Fernandes, "An improved contrast enhancing approach for color-to-grayscale mappings," The Visual Computer, vol. 24, no. 7, pp. 505-514, 2008.

K. Zuiderveld, "Contrast limited adaptive histogram equalization," in Graphics gems IV, S. H. Paul, Ed.: Academic Press Professional, Inc., 1994, pp. 474-485.

Mathworks. (2017, 10.08.2017). Contrast-Limited Adaptive Histogram Equalization. Available:

Abstract views:


Copyright (c) 2017 International Journal of Intelligent Systems and Applications in Engineering

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
© Prof.Dr. Ismail SARITAS 2013-2018     -    Address: Selcuk University, Faculty of Technology 42031 Selcuklu, Konya/TURKEY.