A Machine Learning based Approach for Segmenting Retinal Nerve Images using Artificial Neural Networks
Artificial Intelligence (AI) based Machine Learning (ML) is gaining more attention from researchers. In ophthalmology, ML has been applied to fundus photographs, achieving robust classification performance in the detection of diseases such as diabetic retinopathy, retinopathy of prematurity, etc. The detection and extraction of blood vessels in the retina is an essential part of various diagnosing problems associated with eyes, such as diabetic retinopathy. This paper proposes a novel machine learning approach to segment the retinal blood vessels from eye fundus images using a combination of color features, texture features, and Back Propagation Neural Networks (BPNN). The proposed method comprises of two steps, namely the color texture feature extraction and training the BPNN to get the segmented retinal nerves. Magenta color and correlation-texture features are given as input to the BPNN. The system was trained and tested in retinal fundus images taken from two distinct databases. The average sensitivity, specificity, and accuracy obtained for the segmentation of retinal blood vessels were 0.470%, 0.914%, and 0.903% respectively. Results obtained reveal that the proposed methodology is excellent in automated segmentation retinal nerves. The proposed segmentation methodology was able to obtain comparable accuracy with other methods.
Keywords:machine learning, texture feature, retinal nerves, segmentation, neural networks, feature extraction
A. H. Asad and A. E. Hassaanien, "Retinal Blood Vessels Segmentation Based on Bio-Inspired Algorithm," in Applications of Intelligent Optimization in Biology and Medicine: Current Trends and Open Problems, A.-E. Hassanien, C. Grosan, and M. Fahmy Tolba, Eds. Cham: Springer International Publishing, 2016, pp. 181-215. DOI: https://doi.org/10.1007/978-3-319-21212-8_8
B. Gharnali and S. Alipour, "MRI Image Segmentation Using Conditional Spatial FCM Based on Kernel-Induced Distance Measure," Engineering, Technology & Applied Science Research, vol. 8, no. 3, pp. 2985-2990, Jun. 2018. DOI: https://doi.org/10.48084/etasr.1999
S. Murawwat, A. Qureshi, S. Ahmad, and Y. Shahid, "Weed Detection Using SVMs," Engineering, Technology & Applied Science Research, vol. 8, no. 1, pp. 2412-2416, Feb. 2018. DOI: https://doi.org/10.48084/etasr.1647
Y. L. Ng, X. Jiang, Y. Zhang, S. B. Shin, and R. Ning, "Automated Activity Recognition with Gait Positions Using Machine Learning Algorithms," Engineering, Technology & Applied Science Research, vol. 9, no. 4, pp. 4554-4560, Aug. 2019. DOI: https://doi.org/10.48084/etasr.2952
S. D. Solkar and L. Das, "Survey on retinal blood vessels segmentation techniques for detection of diabetic retinopathy," International Journal of Electronics, Electrical, and Computational Systems, vol. 6, no. 6, 2017.
M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, and M. D. Abramoff, "Comparative study of retinal vessel segmentation methods on a new publicly available database," in Proceedings Medical Imaging 2004: Image Processing, May 2004, vol. 5370, pp. 648-656. DOI: https://doi.org/10.1117/12.535349
J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, "Ridge-based vessel segmentation in color images of the retina," IEEE Transactions on Medical Imaging, vol. 23, no. 4, pp. 501-509, Apr. 2004. DOI: https://doi.org/10.1109/TMI.2004.825627
J. V. B. Soares, J. J. G. 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, Sep. 2006. DOI: https://doi.org/10.1109/TMI.2006.879967
Z. F. Khan, "Automated Segmentation of Lung Parenchyma Using Colour Based Fuzzy C-Means Clustering," Journal of Electrical Engineering & Technology, vol. 14, no. 5, pp. 2163-2169, Sep. 2019. DOI: https://doi.org/10.1007/s42835-019-00224-8
M. M. Fraz et al., "An approach to localize the retinal blood vessels using bit planes and centerline detection," Computer Methods and Programs in Biomedicine, vol. 108, no. 2, pp. 600-616, Nov. 2012. DOI: https://doi.org/10.1016/j.cmpb.2011.08.009
E. Ricci and R. Perfetti, "Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification," IEEE Transactions on Medical Imaging, vol. 26, no. 10, pp. 1357-1365, Oct. 2007. DOI: https://doi.org/10.1109/TMI.2007.898551
Huiqi Li, W. Hsu, Mong Li Lee, and Tien Yin Wong, "Automatic grading of retinal vessel caliber," IEEE Transactions on Biomedical Engineering, vol. 52, no. 7, pp. 1352-1355, Jul. 2005. DOI: https://doi.org/10.1109/TBME.2005.847402
Liang Zhou, M. S. Rzeszotarski, L. J. Singerman, and J. M. Chokreff, "The detection and quantification of retinopathy using digital angiograms," IEEE Transactions on Medical Imaging, vol. 13, no. 4, pp. 619-626, Dec. 1994. DOI: https://doi.org/10.1109/42.363106
Y. Yin, M. Adel, and S. Bourennane, "Retinal vessel segmentation using a probabilistic tracking method," Pattern Recognition, vol. 45, no. 4, pp. 1235-1244, Apr. 2012. DOI: https://doi.org/10.1016/j.patcog.2011.09.019
O. Wink, W. J. Niessen, and M. A. Viergever, "Multiscale vessel tracking," IEEE Transactions on Medical Imaging, vol. 23, no. 1, pp. 130-133, Jan. 2004. DOI: https://doi.org/10.1109/TMI.2003.819920
Y. Yin, M. Adel, and S. Bourennane, "Automatic segmentation and measurement of vasculature in retinal fundus images using probabilistic formulation," Computational and Mathematical Methods in Medicine, vol. 2013, Art no. 260410, 2013. DOI: https://doi.org/10.1155/2013/260410
J. Zhang, H. Li, Q. Nie, and L. Cheng, "A retinal vessel boundary tracking method based on Bayesian theory and multi-scale line detection," Computerized Medical Imaging and Graphics, vol. 38, no. 6, pp. 517-525, Sep. 2014. DOI: https://doi.org/10.1016/j.compmedimag.2014.05.010
B. Zhang, L. Zhang, L. Zhang, and F. Karray, "Retinal vessel extraction by matched filter with first-order derivative of Gaussian," Computers in Biology and Medicine, vol. 40, no. 4, pp. 438-445, Apr. 2010. DOI: https://doi.org/10.1016/j.compbiomed.2010.02.008
L. Gang, O. Chutatape, and S. M. Krishnan, "Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter," IEEE Transactions on Biomedical Engineering, vol. 49, no. 2, pp. 168-172, Feb. 2002. DOI: https://doi.org/10.1109/10.979356
P. Bankhead, C. N. Scholfield, J. G. McGeown, and T. M. Curtis, "Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement," PLOS ONE, vol. 7, no. 3, 2012, Art. no. e32435. DOI: https://doi.org/10.1371/journal.pone.0032435
Y. Wang, G. Ji, P. Lin, and E. Trucco, "Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition," Pattern Recognition, vol. 46, no. 8, pp. 2117-2133, Aug. 2013. DOI: https://doi.org/10.1016/j.patcog.2012.12.014
G. Azzopardi, N. Strisciuglio, M. Vento, and N. Petkov, "Trainable COSFIRE filters for vessel delineation with application to retinal images," Medical Image Analysis, vol. 19, no. 1, pp. 46-57, Jan. 2015. DOI: https://doi.org/10.1016/j.media.2014.08.002
N. Memari, A. R. Ramli, M. I. B. Saripan, S. Mashohor, and M. Moghbel, "Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier," PLOS ONE, vol. 12, no. 12, 2017, Art. no. e0188939. DOI: https://doi.org/10.1371/journal.pone.0188939
B. Fang, W. Hsu, and M. L. Lee, "Reconstruction of vascular structures in retinal images," in Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), Sep. 2003, vol. 2, pp. II-157.
X. You, Q. Peng, Y. Yuan, Y. Cheung, and J. Lei, "Segmentation of retinal blood vessels using the radial projection and semi-supervised approach," Pattern Recognition, vol. 44, no. 10, pp. 2314-2324, Oct. 2011. DOI: https://doi.org/10.1016/j.patcog.2011.01.007
R. M. Haralick, K. Shanmugam, and I. Dinstein, "Textural Features for Image Classification," IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, no. 6, pp. 610-621, Nov. 1973. DOI: https://doi.org/10.1109/TSMC.1973.4309314
S. Hwang and M. Emre Celebi, "Texture Segmentation of Dermoscopy Images using Gabor Filters and G-Means Clustering," in IPCV 2010 : Proceedings of the 2010 International Conference on Image Processing, Computer Vision, & Pattern Recognition, 2010, pp. 882-886, [Online]. Available: http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=26052459.
A. M. Mendonca and A. Campilho, "Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction," IEEE Transactions on Medical Imaging, vol. 25, no. 9, pp. 1200-1213, Sep. 2006. DOI: https://doi.org/10.1109/TMI.2006.879955
P. Dai et al., "A New Approach to Segment Both Main and Peripheral Retinal Vessels Based on Gray-Voting and Gaussian Mixture Model," PLOS ONE, vol. 10, no. 6, Art no. e0127748, 2015. DOI: https://doi.org/10.1371/journal.pone.0127748
T. Chakraborti, D. K. Jha, A. S. Chowdhury, and X. Jiang, "A self-adaptive matched filter for retinal blood vessel detection," Machine Vision and Applications, vol. 26, no. 1, pp. 55-68, Jan. 2015. DOI: https://doi.org/10.1007/s00138-014-0636-z
K. BahadarKhan, A. A. Khaliq, and M. Shahid, "A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding," PLOS ONE, vol. 11, no. 7, 2016, Art. no. e0158996. DOI: https://doi.org/10.1371/journal.pone.0158996
M. Vlachos and E. Dermatas, "Multi-scale retinal vessel segmentation using line tracking," Computerized Medical Imaging and Graphics, vol. 34, no. 3, pp. 213-227, Apr. 2010. DOI: https://doi.org/10.1016/j.compmedimag.2009.09.006
Y. Zhao, Y. Liu, X. Wu, S. P. Harding, and Y. Zheng, "Retinal Vessel Segmentation: An Efficient Graph Cut Approach with Retinex and Local Phase," PLOS ONE, vol. 10, no. 4, 2015, Art. no. e0122332. DOI: https://doi.org/10.1371/journal.pone.0122332
How to Cite
MetricsAbstract Views: 291
PDF Downloads: 207
Copyright (c) 2020 Author
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.