A Review of Algorithms for Retinal Vessel Segmentation

  • Monserrate Intriago Pazmiño Technical University of Madrid
  • Fernando Uyaguari Uyaguari Technical University of Madrid
  • Elizabeth Salazar Jácome Jefatura de Investigación y Vinculación con la Colectividad de la Universidad de las Fuerzas Armadas ESPE Extensión Latacunga
Keywords: Fundus, Fundus analysis, Image analysis, Morphology, Retinal vessels segmentation, Retinopathy, Vessel detection

Abstract

This paper presents a review of algorithms for extracting blood vessels network from retinal images. Since retina is a complex and delicate ocular structure, a huge effort in computer vision is devoted to study blood vessels network for helping the diagnosis of pathologies like diabetic retinopathy, hypertension retinopathy, retinopathy of prematurity or glaucoma.  To carry out this process many works for normal and abnormal images have been proposed recently. These methods include combinations of algorithms like Gaussian and Gabor filters, histogram equalization, clustering, binarization, motion contrast, matched filters, combined corner/edge detectors, multi-scale line operators, neural networks, ants, genetic algorithms, morphological operators. To apply these algorithms pre-processing tasks are needed. Most of these algorithms have been tested on publicly retinal databases. We have include a table summarizing algorithms and results of their assessment.

DOI

Downloads

Download data is not yet available.

References

M. Intriago and J. Crespo del Arco, Diagnóstico semiautomático de la retinopatía de la prematuridad, Latin American Journal of Computing, Systems Engineering, vol. 1, no. 1, pp. 31-35, 2012.

Research Section, Digital Retinal Image for Vessel Extraction (DRIVE),Utrecht.

U. o. Lincoln, Retinal Image Computing &Understanding, 2013. [Online]. Available: http://reviewdb.lincoln.ac.uk/reviewdb/reviewdb.aspx. [Accessed 20 August 2014].

T. Fawcett, An introduction to ROC analysis,Pattern Recogn. Lett, vol. 27, no. 8, pp. 861-874, 2006.

C. Kirbas and Q. Francis, A Review of Vessel Extraction Techniques and Algorithms,ACM Computing Surveys, vol. 36, no. 2, pp. 81-121, 2004.

S. S. Honale and V. S. Kapse, A Review of Methods for Blood Vessel Segmentation in Retinal images,International Journal of Engineering Research and Technology, vol. 1, no. 10, 2012.

S. Kumar Kuri andM. Rabiul Hossain, «Automated Retinal Blood Vessels Extraction Using Optimized Gabor Filter,3rd International Conference on Informatics, Electronics & Vision, 2014.

J. V. B. Soares, J. J. G. Leandro, R. M. Cesar, Jr.,H. F. Jelinek and M. J. Cree, Retinal vessel segmentation using the 2D Gabor waveletand supervised classification,IEEE Trans. Med. Imag, vol. 25, no. 9, p. 1214–1222, 2006.

X. Yin, B. W-H Ng, J. He, Y. Zhang and D. Abbott, Accurate Image Analysis of the Retina Using Hessian Matrix and Binarisation of Thresholded Entropy with Application of Texture Mapping,PLOS ONE, vol. 9, no. 4, 2014.

S. P, Morphological Image Analysis. Principles and Applications, Berlin: Springer Verlag, 1999.

L. Gang , O.Chutatape and S. M. Krishnan, Detection and measurement of retinal vessels in fundus image using amplitude modified second order Gaussian filter,IEEE Trans. Biomed, vol. 49, no. 2, p. 168–172, 2003.

L. Yu, Y. Qi and L. Xuan, Retinal vessel extraction by means of motion contrast, matched filter and combined corner-edge detector, OpticsCommunications, vol. 318, pp. 17-25, 2014.

V. Mohammadi Saffarzadeh, A. Osareh and B. Shadgar, Vessel Segmentation in Retinal Images Using Multi-scale Line Operator and K-Means Clustering,Journal of Medical Signals and Sensors, vol. 4, no. 2, p. 122–129, 2014.

S. Wilfred Franklin and S. Edward Rajan, Retinal vessel segmentation employing ANN technique by Gabor andmoment invariants-based features,Applied Soft Computing, vol. 22, pp. 94-100, 2014.

J. Odstrcilik, R. Kolar, A. Budai, J. Hornegger, J. Jan, J. Gazarek, T. Kubena, P. Cernosek, O. Svoboda and E. Angelopoulou, Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database,IET Image Processing, vol. 7, no. 4, pp. 373-383, 2013.

H. Nezamabadi-pour, S. Saryazdi and E. Rashedi, Edge detection using ant algorithms,Soft Comput, pp. 623-628, 2006.

D.-S. Lu and C.-C. Chen, Edge detection improvement by ant colony optimization,Pattern Recognit. Lett., vol. 29, no. 4, pp. 416-425, 2008.

Y. Liang,. A.-L. Chen and C.-C. Chyu, Application of a hybrid ant colony optimization for the multilevel thresholding in image processing,ICONIP’06, Part II, LNCS, vol. 4233, pp. 1183-1192, 2006.

A. Malisia and H. Tizhoosh, Image thresholdingusing ant colony optimization,The 3rd Canadian Conference on Computer and Robot Vision, no. 2006, p. 26, 2006.

X. Zhao, M.-E. Lee and S.-H. Kim, Improved image thresholding using ant colony optimization algorithm,Int. Conf. Adv. Lang. Process. Web Inf. Technol., pp. 210-215, 2008.

A. Malisia and H. Tizhoosh, Applying ant colony optimization to binary thresholding,IEEE Int. Conf. Image, p. 2409–2415, 2006.

J. Handl and B. Meyer, Ant-based and swarm-based clustering, Swarm Intell, no. 2007, pp. 95-113, 2007.

M. Gökhan Cinsdikici and D. Aydın, Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm,Computer methods and programs in biomedicine, vol. 96, pp. 85-95, 2009.

M. Mitchell, An Introduction to Genetic Algorithms, Cambridge: The MIT, 1997.

M. Al-Rawi and H. Karajeh, Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images,Computer methods and programs in biomedicine, vol. 87, pp. 248-253, 2007.

E. Felipe-Riveron and N. Garcia-Guimeras, Extraction of Blood Vessels in OphthalmicColor Images of Human Retinas,CIARP, pp. 118-126, 2006.

R. Gonzalez and R.E. Woods, Digital Image Processing, Imington, Delaware: Addison-Wesley, 1996.

M. Fraza, P. Remagninoa, A. Hoppea, B. Uyyanonvarab, A. Rudnickac, C. Owenc and S. Barmana, Computermethods and programs in biomedicine,Blood vessel segmentation methodologies in retinal images -A survey, pp. 407-433, 2012.

T. Reuters, Web of Science,[Online]. Available: http://thomsonreuters.com/thomson-reuters-web-of-science/. [Accessed 20 August 2014].

IEEE, “IEEE Xplore Digital Library, [Online]. Available: http://ieeexplore.ieee.org/Xplore/home.jsp. [Accessed 20 August 2014].

Google, Google Académico,[Online]. Available: http://scholar.google.es/. [Accessed 20 August 2014].

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2 ed., Prentice Hall, 2002.

Published
2014-09-17
How to Cite
[1]
M. Intriago Pazmiño, F. Uyaguari Uyaguari, and E. Salazar Jácome, “A Review of Algorithms for Retinal Vessel Segmentation”, LAJC, vol. 1, no. 1, p. 5, Sep. 2014.
Section
Research Articles for the Regular Issue