OJMI  Vol.5 No.4 , December 2015
An Improved Method for Automatic Retinal Blood Vessel Vascular Segmentation Using Gabor Filter
ABSTRACT
Early detection of Non-Proliferative Diabetic Retinopathy (NDPR) is currently a highly interested research area in biomedical imaging. Ophthalmologists discover NDPR by observing the configuration of the vessel vascular network deliberately. Therefore, a computerized automatic system for the segmentation of vessel system will be an assist for ophthalmologists in order to detect an early stage of retinopathy. In this research, region based retinal vascular segmentation approach is suggested. In the steps of processing, the illumination variation of the fundus image is adjusted by using the point operators. Then, the edge features of the vessels are enhanced by applying the Gabor Filter. Finally, the region growing method with automatic seed point selection is used to extract the vessel network from the image background. The experiments of the proposed algorithm are conducted on DRIVE dataset, which is an open access dataset. Results obtain an accuracy of 94.9% over the dataset that has been used.

Cite this paper
Karunanayake, N. and Kodikara, N. (2015) An Improved Method for Automatic Retinal Blood Vessel Vascular Segmentation Using Gabor Filter. Open Journal of Medical Imaging, 5, 204-213. doi: 10.4236/ojmi.2015.54026.
References
[1]   Onkaew, D., Turior, R., et al. (2011) Automatic Extraction of Retinal Vessels Based on Gradient Orientation Analysis. 8th International Joint Conference on Computer Science and Software Engineering (JCSSE), Thailand, 5 November 2011, 102-107.

[2]   Rattathanapad, S., Uyyanonvara, B., et al. (2011) Vessel Segmentation in Retinal Images Using Graph Theoretical Vessel Tracking. Conference on Machine Vision Applications, Japan, 13-15 June 2011, 548-551.

[3]   Hou, Y. (2014) Automatic Segmentation of Retinal Blood Vessels Based on Improved Multiscale Line Detection. Journal of Computing Science and Engineering, 8, 119-128.
http://dx.doi.org/10.5626/JCSE.2014.8.2.119

[4]   Jeyasri, K., Subathra, P., et al. (2013) Detection of Retinal Blood Vessels for Disease Diagnosis. International Journal of Advanced Research in Computer Science and Software Engineering, 3, 6-12.

[5]   Jahan, N. (2014) Detection and Segmentation Digital Retinal Blood Vessels Using Neural Network. International Journal of Engineering Research and Reviews, 2, 36-43.

[6]   Osareh, A. and Shadgar, B. (2009) Automatic Blood Vessel Segmentation in Color Images of Retina. Iranian Journal of Science and Technology, 33, 191-206.

[7]   Hamza, A., Taher, A., et al. (2013) An Improved Ant Colony System for Retinal Blood Vessel Segmentation. Federated Conference on Computer Science and Information Systems, Poland, 8-11 September 2013, 199-205.

[8]   Intriago, M., Uyaguari, F., et al. (2014) A Review of Algorithms for Retinal Vessel Segmentation. Latin American Journal of Computing, 1, 1140-1144.

[9]   Kharghanian, R. and Ahmadyfard, A. (2012) Retinal Blood Vessel Segmentation Using Gabor Wavelet and Line Operator. International Journal of Machine Learning and Computing, 2, 593-597.
http://dx.doi.org/10.7763/IJMLC.2012.V2.196

[10]   Dey, N., Bardhan, A., et al. (2012) FCM Based Blood Vessel Segmentation Method for Retinal Images. International Journal of Computer Science and Network, 1.

[11]   Joshi, P.S. (2012) Retinal Blood Vessel Segmentation. International Journal of Engineering and Innovative Technology (IJEIT), 1, 175-178.

[12]   Rattathanapad, S., Uyyanonvara, B., et al. (2011) Vessel Segmentation in Retinal Images Using Graph-Theoretical Vessel Tracking. Proceedings of the Conference on Machine Vision Applications, Nara, 13-15 June 2011, 548-551.

[13]   Sukkaew, L., Uyyanonvara, B., et al. (2004) Automated Vessels Detection on Infant Retinal Images. Proceedings of the ICCAS, Bangkok, 25-27 August 2004, 321-325.

[14]   Siddalingaswamy, P.C. and Prabhu, K.G. (2010) Automatic Detection of Multiple Oriented Blood Vessels. Journal of Biomedical Science and Engineering, 3, 101-107.
http://dx.doi.org/10.4236/jbise.2010.31015

[15]   Onkaew, D., Turior, R., et al. (2011) Automatic Extraction of Retinal Vessels Based on Gradient Orientation Analysis. Proceedings of the Eighth International Joint Conference on Computer Science and Software Engineering (JCSSE), Nakhon Pathom, 11-13 May 2011, 102-107.

[16]   Usman, M. and Khan, S.A. (2013) Multilayered Thresholding Based Blood Vessel Segmentation for Screening of Diabetic Retinopathy. Engineering with Computers, 29, 165-173.
http://dx.doi.org/10.1007/s00366-011-0253-7

[17]   Niemeijer, M. and Van Ginneken, B. (2002)
http://www.isi.uu.nl/Research/Databases/DRIVE/.

[18]   Dizdaroglu, B., Cansizoglu, E., et al. (2014) Structure-Based Level Set Method for Automatic Retinal Vasculature Segmentation. EURASIP Journal on Image and Video Processing, 2014, 39.
http://dx.doi.org/10.1186/1687-5281-2014-39

[19]   Lathen, G., Jonasson, J., et al. (2010) Blood Vessel Segmentation Using Multi-Scale Quadrature Filtering. Pattern Recognition Letters, 31, 762-767.
http://dx.doi.org/10.1016/j.patrec.2009.09.020

[20]   Budai, A., Bock, R., et al. (2013) Robust Vessel Segmentation in Fundus Images. International Journal of Biomedical Imaging, 2013, 1-11.
http://dx.doi.org/10.1155/2013/154860

[21]   Marin, D., Aquino, A., et al. (2010) A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features. IEEE Transactions on Medical Imaging, 30, 146-158.

[22]   Soares, J.V., Leandro, J.J., et al. (2006) Retinal Vessel Segmentation Using the 2-D Gabor Wavelet and Supervised Classification. IEEE Transactions on Medical Imaging, 25, 1214-1222.

[23]   Marin, D., Kevin, J.C., et al. (2010) A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features. IEEE Transactions on Medical Imaging, 30, 146-158.

[24]   Walter, T. and Chutatape, O. (2002) A Contribution of Image Processing to the Diagnosis of Diabetic Retinopathy. IEEE Transactions on Image Processing, 21, 1236-1243.
http://dx.doi.org/10.1109/TMI.2002.806290

[25]   Sinthanayothin, C., Boyce, J.F., Cook, H.L. and Williamson, T.H. (1999) Automated Localization of the Optic Disk, Fovea, and Retinal Blood Vessels from Digital Colour Fundus Images. British Journal of Ophthalmology, 83, 902-910.
http://dx.doi.org/10.1136/bjo.83.8.902

[26]   Jiang, X. and Mojon, D. (2003) Adaptive Local Thresholding by Verification Based Multithreshold Probing with Application to Vessel Detection in Retinal Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 131-137.
http://dx.doi.org/10.1109/TPAMI.2003.1159954

[27]   Jain, A. (1989) Fundamentals of Digital Processing. Prentice Hall, New York.

[28]   Rossant, F., Badellino, M., et al. (2011) A Morphological Approach for Vessel Segmentation in Eye Fundus Images, with Quantitative Evaluation. Journal of Medical Imaging and Health Informatics, 1, 42-49.
http://dx.doi.org/10.1166/jmihi.2011.1006

[29]   Bhattacharya, D., Devi, J., et al. (2013) Brain Image Segmentation Technique Using Gabor Filter Parameter. American Journal of Engineering Research (AJER), 2, 127-132.

[30]   Yamani, L.S., Asif, K., et al. (2015) A Noval Method for Extraction of Retinal Blood Vessels Using Gabor Filter and Generalized Linear Model. Global Journal of Trends in Engineering, 2, 209-215.

[31]   Gwetu, M.V., Tapamo, J.-R., et al. (2014) Segmentation of Retinal Blood Vessels Using Normalized Gabor Filters and Automatic Thresholding. South African Computer Journal, 55, 12-24.

[32]   Kamdi, S. and Krishna, R.K. (2011) Image Segmentation and Region Growing Algorithm. International Journal of Computer Technology and Electronics Engineering, 2, 103-107.

[33]   Dougherty, G. (2009) Digital Image Processing for Medical Applications. Cambridge University Press, New Delhi.

[34]   Melouah, A. and Amirouche, R. (2014) Comparative Study of Automatic Seed Selection Methods for Medical Image Segmentation by Region Growing Technique. In: Recent Advances in Biology, Biomedicine and Bioengineering, InTech Publisher, Rijeka, 91-97.

 
 
Top