A Composite Feature Set Based Blood Vessel Segmentation in Retinal Images through Supervised Learning


Y. Madhu Sudhana Reddy,R. S. Ernest Ravindran,




retinal vessel segmentation,Gabor filter,Support vector machine,Gradient features,Correlation Accuracy,


Retinal image analysis has gained a significant research interest due to its widespread applicability in the diagnosis of different eye related diseases. This paper focused in the analysis of Diabetic Retinopathy through different features (Optic Disk, Retinal Vessels, and Exudates etc.,) of retinal image. Towards this objective, a new Retinal Vessel Segmentation mechanism is introduced in this paper. The proposed mechanism accomplished the Gabor Filter for Feature Extraction and Support Vector Machine Algorithm for classification. Here the Gabor Filter ensures a more resilience to the scaling and orientation issues in the retinal image. Afterwards, a feature set consists of thirteen features is extracted from retinal image to provide a proper differentiation between the image pixels and background pixels. Based on these features, the SVM classifier classifies the vessel pixels and background pixels more effectively which improves the classification accuracy and reduces false positive rate. An extensive simulation carried out over the proposed approach through two standard datasets, DRIVE and STARE reveals the outstanding performance with respect to the performance metrics sensitivity, specificity and accuracy.


I. A. Bhuiyan, B. Nath, J. Chua, and R. Kotagiri, “Blood vessel
segmentation from color retinal images using unsupervised texture
classification,” in 2007 IEEE International Conference on Image
Processing, vol. 5, pp. 521–524, 2007.
II. A. Budai, G. Michelson, and J. Hornegger, “Multiscale blood vessel
segmentation in retinal fundus
images,” Proc. Bildverarbeitungfr die Med., pp. 261–265, March 2010.
III. A. Budai, R. Bock, A. Maier, J. Hornegger, and G. Michelson, “Robust
vessel segmentation in fundus images,” International Journal of
Biomedical Imaging, no. 154860, 2013.
IV. A.Fathi, A.R.N Naghsh-Nilchi, Automatic wavelet-based retinal blood
vessels segmentation and vessel diameter estimation ,Biomed. Signal
Process. Control 8(1)(2013) 71-80
V. A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever,
“Multiscale vessel enhancement filtering,” in International Conference on
Medical Image Computing and Computer- Assisted Intervention, pp.
130–137, Springer, Berlin Heidelberg, 1998.
VI. A. Hoover, “Locating blood vessels in retinal images by piecewise
threshold probing of a matched filter response,” IEEE Transactions on
Medical Imaging, vol. 19, no. 3, pp. 203–210, 2000.
VII. A.Hoover, Structured Analysis of the Retina
STARE,http://www.ces.clemson.edu/~ahoover/stare/, 2015.
VIII. B D Barkana, “Performance analysis of descriptive statistical features in
retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier
fusion”, Knowledge-Based Systems, Volume 118, 15February 2017,
Pages 165-176
IX. B.R. Mcclintic, J.I. Mcclintic, B.S. Ba, J.D. Bisognano, R.C. Block, A
relationship between microvascular abnormalities and coronary disease –
a review, Am. J. Med. 123 (4) (2011) 1–12.

X. D. Marin, A. Aquino, M. Gegundez-Arias, and J. Bravo, “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, vol. 30, no. 1, pp. 146–158, 2011.
XI. F. Zana and J. C. Klein, “Segmentation of vessel-like pattern using
mathematical morphology and curvature evaluation,” IEEE Transactions
on Image Processing, vol. 10, no. 7, pp. 1010–1019, 2001.
XII. G. Azzopardi, N. Strisciuglio, M. Vento, and N. Petkov, “Trainable
COSFIRE filters for vessel delineation with application to retinal
images,” Med. Image Anal., vol. 19, no. 1, pp. 46–57, 2015.
XIII. G. B. Kande, P. V. Subbaiah, and T. S. Savithri, “Unsupervised fuzzy
based vessel segmentation in pathological digital fundus images,” Journal
of Medical Systems, vol. 34, no. 5, pp. 849–858, 2010.
XIV. Liskowski P, Krawiec K. Segmenting Retinal Blood Vessels with Deep
Neural Networks. IEEE Transactions on Medical Imaging. 2016.
XV. Mohan V, Shah SN, Joshi SR, Seshiah V, Sahay BK, Banerjee S, Current
status of management, control, complications and psychosocial aspects of
patients with diabetes in India: Results from the DiabCare India 2011
Study. Indian J EndocrinolMetab 2014; 18:370-8.
XVI. N. Memari, A R Ramil, M I B saripan, S. Mashohor, M Moghbel,
Supervised retinal vessel segmentation from color fundus images based
on matched filtering and AdaBoost classifier, PLoS ONE 12(12):
e0188939, 2017.
XVII. Orlando J, Prokofyeva E, Blaschko M. A Discriminatively Trained Fully
Connected Conditional Random Field Model for Blood Vessel
Segmentation in Fundus Images. IEEE Transactions on Biomedical
Engineering. 2016.
XVIII. Peter Sekulic, M Bajceta, S Dukanovic, “Retinal blood vessels
segmentation using support vector machine and modified line detector”,
22nd International Scientific-Professional Conference
Information Technology, 2017.
XIX. P. Rani, N. Priyadarshini, E. R. Rajkumar, and K. Rajamani, “Retinal
vessel segmentation under
pathological conditions using supervised machine learning,” in 2016
International Conference on, Systems in Medicine and Biology (ICSMB),
pp. 62–66, 2016.
XX. Roychowdhury S, Koozekanani DD, Parhi KK. Blood vessel
segmentation of fundus images by major vessel extraction and subimage
classification. IEEE journal of biomedical and health informatics. 2015
May; 19(3):1118–28. pmid:25014980
XXI. S. A. A. Shah, T. B. Tang, I. Faye, and A. Laude, “Blood vessel
segmentation in color fundus images based on regional and Hessian
features,” Graefe’s Archive for Clinical and Experimental
Ophthalmology, pp. 1– 9, 2017.

XXII. S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum,
“Detection of blood vessels in retinal images using two-dimensional
matched filters,” IEEE Transactions on Medical Imaging, vol. 8, no. 3,
pp. 263–269, 1989.
XXIII. SoumiaBelhadi and NadjiaBenblidia, “Automated Retinal Vessel
Segmentation using Entropic Thresholding Based Spatial Correlation
Histogram of Gray Level Images”, The International Arab Journal of
Information Technology, Vol. 12, No. 5, September 2015.
XXIV. SumathiThangaraj, VivekanandamPeriyasamy, RavikanthBalaji,
“Retinal vessel segmentation using neural network”, IET Image
Processing, Volume: 12, Issue: 5, pp.669-678. 2018
XXV. T. Chakraborti, D. K. Jha, A. S. Chowdhury, and X. Jiang, “A selfadaptive
matched filter for retinal blood vessel detection,” Machine
Vision and Applications, pp. 1–14, 2014.
XXVI. T. Jintasuttisak, and S. Intajag, “Color Retinal Image Enhancement by
Rayleigh Contrast-Limited Adaptive Histogram Equalization”, In: Proc.
of International Conf. on Control, Automation and Systems, Korea, pp.
22-25 2014.
XXVII. T. Mapayi, S. Viriri, and J. R. Tapamo, “Adaptive thresholding technique
for retinal vessel segmentation based on glcm-energy information,”
Computational and Mathematical Methods in Medicine, vol. 2015,
Article ID 597475, 11 pages, 2015.
XXVIII. T. Walter and J.-C. Klein, “Segmentation of color fundus images of
the human retina: detection of the optic disc and the vascular tree using
morphological techniques,” Medical Data Analysis, pp. 282–287, 2001.
XXIX. W. S. Oliveira, J. V. Teixeira, T. I. Ren, G. D. C. Cavalcanti, and J.
Sijbers, “Unsupervised retinal vessel segmentation using combined
filters,” PLoS One, vol. 11, no. 2, article e0149943, 2016.
XXX. X. Jiang and D. Mojon, “Adaptive local thresholding by verificationbased
multi-threshold probing with application to vessel detection in
retinal images,” IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 25, no. 1, pp. 131–137, 2003.
XXXI. Y. MadhuSudhana Reddy, R. S. Ernest Ravindran, Kakarla. Hari
Kishore, “Spatial Mutual Relationship Based Retinal Image Contrast
Enhancement for Efficient Diagnosis of Diabetic Retinopathy”,
International journal of Intelligent Engineering systems, Vol.11, issue.4,
XXXII. Y. Qian Zhao, X. Hong Wang, X. Fang Wang, F.Y. Shih, Retinal vessels
segmentation based on level set and region growing, Pattern Recognit. 47
(7) (2014) 2437–2446.
XXXIII. Y. Wang, G. Ji, P. Lin, E. Trucco, Retinal vessel segmentation using
multiwavelet kernels and multiscale hierarchical decomposition,
PatternRecognit. 46 (8) (2013) 2117–2133.

XXXIV. 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,
XXXV. 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, Article ID 260410, 16 pages, 2013.
XXXVI. Z. Xiao, M. Adel, and S. Bourennane, “Bayesian method with spatial
constraint for retinal vessel segmentation,” Computational and
Mathematical Methods in Medicine, vol. 2013, Article ID 401413, 9
pages, 2013.

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