Retinal Blood Vessels Segmentation using Fréchet PDF and MSMO Method


  • Sushil Kumar saroj MMMUT gorakhpur
  • Rakesh Kumar
  • Nagendra Pratap Singh


Blood vessels of retina contain information about many severe diseases like glaucoma, hypertension, obesity, diabetes etc. Health professionals use this information to detect and diagnose these diseases. Therefore, it is necessary to segment retinal blood vessels. Quality of retinal image directly affects the accuracy of segmentation. Therefore, quality of image must be as good as possible. Many researchers have proposed various methods to segment retinal blood vessels. Most of the researchers have focused only on segmentation process and paid less attention on pre processing of image even though pre processing plays vital role in segmentation. The proposed method introduces a novel method called multi-scale switching morphological (MSMO) for pre processing and Fréchet match filter for retinal vessel segmentation. We have experimentally tested and verified the proposed method on DRIVE, STARE and HRF data sets. Obtained outcome demonstrate that performance of the proposed method has improved substantially. The cause of improved performance is the better pre processing and segmentation methods.


Image enhancement, MSMO Operator, Segmentation, Fréchet PDF


N. Memari, A. R. Ramli, M. I. B. Saripan, S. Mashohor, M. Moghbel "Retinal Blood Vessel Segmentation by Using Match Filtering and Fuzzy C‑means Clustering with Integrated Level Set Method for Diabetic Retinopathy Assessment" Journal of Medical and Biological Engineering, vol. 38, pp. 713-31, 2019.

J. Almotri, K. Elleithy, A. Elleithy “Retinal Vessels Segmentation Techniques and Algorithms: A Survey” Applied Sciences, MDPI, vol. 8, is. 155, pp. 1-31, 2018.

J. Dash, N. Bhoi "Detection of Retinal Blood Vessels from Ophthalmoscope Images Using Morphological Approach" Electronic Letter on Computer Vision and Image Analysis, vol. 16, pp. 1-14, 2017.

J. Odstrcilik, R. Kolar, A. Budai, J. Hornegger, J. Jan, J. Gazarek, T. Kubena, P. Cernosek, O. Svoboda, E. Angelopoulou "Retinal vessel segmentation by improved match filtering: evaluation on a new high-resolution fundus image database" IET Image Process., vol. 7, is. 4, pp.373–383, 2013.

L. Zhou, Q. Yu, X. Xu, Y. Gu, J. Yang "Improving dense conditional random field for retinal vessel segmentation by discriminative feature learning and thin-vessel enhancement" Computer Methods and Programs in Biomedicine, vol. 148, pp. 13-25, 2017.

S. K. Saroj, R. Kumar, N. P. Singh "Fréchet PDF based Match Filter Approach for Retinal Blood Vessels Segmentation" Computer Methods and Programs in Biomedicine, vol. 194, pp. 1-17, 2020.

N. P. Singh, R. Srivastava “Retinal blood vessels segmentation by using Gumbel probability distribution function based match filter” Computer Methods and Programs in Biomedicine, vol. 129, pp. 40-50, 2016.

D. Kaba, C. Wang, Y. Li, A. Salazar-Gonzalez, X. Liu, A. Serag “Retinal blood vessels extraction using probabilistic modelling” Health Information Science Systems, vol. 2, 2014.

H. Zolfagharnasab, A. R. Naghsh-Nilchi “Cauchy based match filter for retinal vessels detection” Journal of Medical Signals and Sensors, vol. 4, is. 1, pp. 1-26, 2014.

M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, S. A. Barman “Blood vessel segmentation methodologies in retinal images–a survey” Computer Methods and Programs in Biomedicine, vol. 108, is. 1, pp. 407–433, 2012.

N. P. Singh, R. Srivastava “Extraction of Retinal Blood Vessels by Using an Extended Match Filter Based on Second Derivative of Gaussian” Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, vol. 89, pp. 269–277, 2019.

J. Seo, S. D. Kim “Novel pca-based color-to-gray image conversion” Image Processing (ICIP), International Conference of IEEE, pp. 2279–2283, 2013.

K. S. Sreejini, V. K. Govindan "Improved multiscale match filter for retina vessel segmentation using PSO algorithm" Egyptian Informatics Journal, vol. 16, is. 3, pp. 253–260, 2015.

Y. Chen “A Labeling-Free Approach to Supervising Deep Neural Networks for Retinal Blood Vessel Segmentation” arXiv 2017, arXiv:1704.07502, 2017.

B. S. Lam, Y. Gao, A. W. C. Liew “General retinal vessel segmentation using regularization-based multiconcavity modelling” Medical Imaging, IEEE Transactions, vol. 29, is. 7, pp. 1369–1381, 2010.

Z. L. Szpak, J. R. Tapamo "Automatic and Interactive Retinal Vessel Segmentation" South African Computer Journal, pp. 1-8, 2008.

N. Salem, A. Shams, H. Malik “Medical image enhancement based on histogram algorithms” International Learning and Technology Conference, vol. 163, pp. 300-311, 2019.

S. S. Negi, B. Gupta “Survey of Various Image Enhancement Techniques in Spatial Domain Using MATLAB” International Journal of Computer Applications, pp. 8-18, 2014.

M. A. Amin, H. Yan “High speed detection of retinal blood vessels in fundus image using phase congruency” Soft Computing, vol. 15, is. 6, pp. 1217–1230, 2011.

D. Marín, A. Aquino, M. E. Gegúndez-Arias, J. M. Bravo “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features” Medical Imaging, IEEE Transactions, vol. 30, is.1, pp. 146–158, 2011.

R. GeethaRamani, L. Balasubramanian “Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis. Retinal blood vessel segmentation in fundus images" Bioinformatics and Biomedical Engineering, vol.5, pp. 1-16, 2015.

K. Zuiderveld “Contrast Limited Adaptive Histogram Equalization” Graphics Gems; Academic Press Professional, Inc.: San Diego, CA, USA; pp. 474–485, ISBN 0-12-336155-9, 1994.

N. Solouma, A.-B. M. Youssef, Y. Badr, Y. M. Kadah, “Real-time retinal tracking for laser treatment planning and administration” Medical Imaging, International Society for Optics and Photonics, pp. 1311–1321, 2001.

Y. Wang, S. C. Lee “A fast method for automated detection of blood vessels in retinal images” Signals, Systems & Computers, Conference Record of the Thirty-First Asilomar Conference, vol. 2, pp. 1700–1704, 1997.

A. Pinz, S. Bernögger, P. Datlinger, A. Kruger “Mapping the human retina” Medical Imaging, IEEE Transactions, vol. 17, is. 4, pp. 606–619, 1998.

N. P. Singh, R. Srivastava "Weibull Probability Distribution Function-Based Match Filter Approach for Retinal Blood Vessels Segmentation" Advances in Computational Intelligence, Advances in Intelligent Systems and Computing, pp. 427-437, 2017.

G. B. Kande, P. V. Subbaiah, T. S. Savithri “Unsupervised fuzzy based vessel segmentation in pathological digital fundus images” Journal of medical systems, vol. 34, is. 5, pp. 849–858, 2010.

M. Al-Rawi, M. Qutaishat, M. Arrar “An improved match filter for blood vessel detection of digital retinal images” Computers in Biology and Medicine, vol. 37, is. 2, pp. 262–267, 2007.

G. Tascini, G. Passerini, P. Puliti, P. Zingaretti “Retina vascular network recognition” Medical Imaging, 1993.

A. Hoover, V. Kouznetsova, M. Goldbaum “Locating blood vessels in retinal images by piecewise threshold probing of a match filter response” Medical Imaging, IEEE Transactions, vol. 19, is. 3, pp. 203–210, 2000.

Y. Zhang, W. Hsu, M. L. Lee “Detection of retinal blood vessels based on nonlinear projections” Journal of Signal Processing Systems, vol. 55, is. 1, pp. 103–112, 2009.

N. R. Pal, S. K. Pal “Entropic thresholding” Signal processing, vol. 16, no. 2, pp. 97–108, 1989.

T. Chanwimaluang, G. Fan “An efficient blood vessel detection algorithm for retinal images using local entropy thresholding” Circuits and Systems, Proceedings, International Symposium, vol. 5, pp. 21–23, 2003.

J. Staal, M.D. Abramoff, M. Niemeijer, M.A. Viergever, B. van Ginneken “Ridge- based vessel segmentation in color images of the retina” IEEE Trans. Med. Imaging vol. 23, is. 4, pp. 501–509, 2004.

M. A. Palomera-Pérez, M. E. Martinez-Perez, H. Benítez-Pérez, J. L. Ortega-Arjona “Parallel multiscale feature extraction and region growing application in retinal blood vessel detection” Information Technology in Biomedicine, IEEE Transactions, vol. 14, is. 2, pp. 500–506, 2010.

R. Vega, G. Sanchez-Ante, L.E. Falcon-Morales, H. Sossa, E. Guevara “Retinal vessel extraction using lattice neural networks with dendritic processing” Com- put. Biol. Med. vol. 58, pp. 20–30, 2015.

C. Becker, R. Rigamonti, V. Lepetit, P. Fua “Supervised feature learning for curvilinear structure segmentation” Proc. MICCAI, pp. 526-533, 2013.

A. Fathi, A R. Naghsh-Nilchi “Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation” Biomed. Signal Process. Control, vol. 8, is. 1, pp.71–80, 2013.

S. Xie, Z. Tu “Holistically-nested edge detection” Proc. ICCV, pp. 1395-1403, 2015.

K. K. Maninis, J. Pont-Tuset, P. Arbeláez, L. V. Gool “Deep retinal image understanding” Proc. MICCAI, pp. 140-148, 2016.

J. V. B. Soares, J. J. G. Leandro, R. M. Cesar, H. F. Jelinek, M. J. Cree “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification” IEEE Trans. Med. Image., vol. 25, is. 9, pp. 1214-1222, 2006.

R. J. Chalakkal, W. H. Abdulla “Improved Vessel Segmentation Using Curvelet Transform and Line Operators” Proceedings, APSIPA Annual Summit and Conference, pp. 2041-2046, 2018.

H. Fu, Y. Xu, D. W. K. Wong, J. Liu “Retinal vessel segmentation via deep learning network and fully connected conditional random fields” Proceedings of the IEEE 13th International Symposium on Biomedical Imaging, Prague, Czech Republic, pp. 698–701, 2016.

S. A. Khowaja, P. Khuwaja, I. A. Ismaili “A framework for retinal vessel segmentation from fundus images using hybrid feature set and hierarchical classification” Signal Image Video Process, vol. 13, pp. 379–387, 2019.

M. Zardadi, N. Mehrshad, S. M. Razavi “Unsupervised Segmentation of Retinal Blood Vessels Using the Human Visual System Line Detection Model” Journal of Information Systems and Telecommunication, vol. 4, is. 2, pp. 125-133, 2016.

J. Dash, N. Bhoi “A thresholding-based technique to extract retinal blood vessels from fundus images” Future Computing and Informatics Journal, pp. 1-7, 2017.

N. Tamim, M. Elshrkawey, G. A. Azim, H. Nassar “Retinal Blood Vessel Segmentation Using Hybrid Features and Multi-Layer Perceptron Neural Networks” Symmetry, MDPI, pp. 1-27, 2020.




Download data is not yet available.