A Rule Based Segmentation Approaches to Extract Retinal Blood Vessels in Fundus Image
AbstractThe physiological structures of the retinal blood vessel are one of the key features that visible in the retinal images and contain the information associate with the anatomical abnormalities. It is accepted all over the world to judge the cardiovascular and retinal disease. To avoid the risk of visual impairment, appropriate vessel segmentation is mandatory. Here has proposed a segmentation algorithm that efficiently extracts the blood vessels from the retinal fundus image. The proposed segmentation algorithm is performed Lab and Principle Component (PC) based gray level conversion, Contrast Limited Adaptive Histogram Equalization (CLAHE), morphological operations, Local Property-Based Pixel Correction (LPBPC). For appropriate detection proposed vessels correction algorithm LPBPC that check the feature of the vessels and remove the wrong vessel detection. To measure the appropriateness of the proposed algorithm, the experimental results are compared with the corresponding ground truth images. The experimental results have shown that the proposed blood vessel algorithm is more accurate than the existing algorithms.
. D. Pascolini and S. P. Mariotti, “Global estimates of visual impairment: 2010,” British Journal of Ophthalmology, vol. 96, no. 5, pp. 614–618, 2012.
. P. Mitchell, R. G. Cumming, K. Attebo, and J. Panchapakesan, “Prevalence of cataract in australia: the blue mountains eye study,” Ophthalmology, vol. 104, no. 4, pp. 581–588, 1997.
. N. Congdon, J. Vingerling, B. Klein, S. West, D. Friedman, J. Kempen, B. O’Colmain, S. Wu, and H. Taylor, “Prevalence of cataract and pseudophakia/aphakia among adults in the united states.” Archives of ophthalmology (Chicago, Ill.: 1960), vol. 122, no. 4, pp. 487–494, 2004.
. J. J. Kanski and A. Kubicka-Trzáska, Clinical ophthalmology: a selfassessment companion, 1st ed. Edinburgh, New York: Elsevier Churchill Livingstone, 2007.
. J.-J. Yang, J. Li, R. Shen, Y. Zeng, J. He, J. Bi, Y. Li, Q. Zhang, L. Peng, and Q. Wang, “Exploiting ensemble learning for automatic cataract detection and grading,” Computer methods and programs in biomedicine, vol. 124, pp. 45–57, 2016.
. J. A. Mobley and R. W. Brueggemeier, “Increasing the dna damage threshold in breast cancer cells,” Toxicology and applied pharmacology, vol. 180, no. 3, pp. 219–226, 2002.
. B. E. K. Klein, R. Klein, K. L. P. Linton, Y. L. Magli, and M. W. Neider, “Assessment of cataracts from photographs in the beaver dam eye study,” Ophthalmology, vol. 97, no. 11, pp. 1428–1433, 1990.
. Soares, J.V.; Leandro, J.J.; Cesar, R.M.; Jelinek, H.F.; Cree, M.J. Retinal vessel segmentation using the 2- D Gabor wavelet and, supervised classification. IEEE Trans. Med. Imaging 2002, 25, 1214–1222.
. Ricci, E.; Perfetti, R. Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imaging 2007, 26, 357–1365.
. Marín, D.; Aquino, A.; Gegúndez-Arias, M.E.; Bravo, J.M. A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imaging 2011, 30, 146–158.
. Tolias, Y.A.; Panas, S.M. A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans. Med. Imaging 1998, 17, 263–273.
. Niemeijer, M.; Staal, J.; van Ginneken, B.; Loog, M.; Abramoff, M.D. Comparative study of retinal vessel segmentation methods on a new publicly available database. JMI 2004, 5370, 648–656.
. Salem, S.A.; Salem, N.M.; Nandi, A.K. Segmentation of retinal blood vessels using a novel clustering algorithm (RACAL) with a partial supervision strategy. Med. Biol. Eng. 2007, 45, 261–273.
. Chaudhuri, S.; Chatterjee, S.; Katz, N.; Nelson, M.; Goldbaum, M. Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging 1989, 8, 263–269.
. Al-Rawi, M.; Qutaishat, M.; Arrar, M. An improved matched filter for blood vessel detection of digital retinal images. Comput. Biol. Med. 2007, 37, 262–267.
. Cinsdikici, M.G.; Aydin, D. Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm. Comput. Methods Programs Biomed. 2009, 96, 85–95.
. Zhang, B.; Zhang, L.; Zhang, L.; Karray, F. Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput. Biol. Med. 2010, 40, 438–445.
. Odstrcilik, J.; Kolar, R.; Budai, A.; Hornegger, J.; Jan, J.; Gazarek, J.; Kubena, T.; Cernosek, P.; Svoboda, O.; Angelopoulou, E. Retinal vessel segmentation by improved matched filtering: Evaluation on a new high-resolution fundus image database. IET Image Process. 2013, 7, 373–383.
. Hoover, A.D.; Kouznetsova, V.; Goldbaum, M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 2000, 19, 203–210.
. Jiang, X.; Mojon, D. Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Trans. Pattern Anal. Mach. Intell. 2003, 25, 131–137.
. Reza, A.W.; Eswaran, C.; Hati, S. Automatic Tracing of Optic Disc and Exudates from Color Fundus Images Using Fixed and Variable Thresholds. J. Med. Syst. 2009, 33, 73, doi:10.1007/s10916-008-9166-4.
. Reza, A.W.; Eswaran, C.; Hati, S. Diabetic retinopathy: A quadtree based blood vessel detection algorithm using RGB components in fundus images. J. Med. Syst. 2008, 32, 147–155.
. Serra, J. Image Analysis and Mathematical Morphology, v. 1; Academic Press: Cambridge, MA, USA, 1982.
. Zana, F.; Klein, J.C. Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans. Image Process. 2001, 10, 1010–1019.
. Heneghan, C.; Flynn, J.; O’Keefe, M.; Cahill, M. Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image analysis. Med. Image Anal. 2002, 6, 407–429.
. Yang, Y.; Huang, S.; Rao, N. An automatic hybrid method for retinal blood vessel extraction. Int. J. Appl. Math. Comput. Sci. 2008, 18, 399–407.
. Mehrotra, A.; Tripathi, S.; Singh, K.K.; Khandelwal, P. Blood Vessel Extraction for retinal images using morphological operator and KCN clustering. In Proceedings of the 2014 IEEE International Advance Computing Conference (IACC), Gurgaon, India, 21–22 February 2014; pp. 1142–1146, doi:10.1109/IAdCC.2014.6779487.
. Miri, M.S.; Mahloojifar, A. Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction. IEEE Trans. Bio-Med. Eng. 2011, 58, 1183–1192.
. Bharkad, S. Automatic segmentation of blood vessels in retinal image using morphological filters. ICSCA 2017, 132–136, doi:10.1145/3056662.3056710.
. Yavuz, Z.; Köse, C. Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification. J. Healthc. Eng. 2017, doi:10.1155/2017/4897258.
. Gao, X.; Bharath, A.; Stanton, A.; Hughes, A.; Chapman, N.; Thom, S. A method of vessel tracking for vessel diameter measurement on retinal images. In Proceedings of the 2001 International Conference on Image Processing (Cat. No.01CH37205), Thessaloniki, Greece, 7–10 October 2001; Volume 2, pp. 881–884.
. Liu. I.; Sun, Y. Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme. IEEE Trans. Med. Imaging 1993, 12, 334–341.
. Delibasis, K.K.; Kechriniotis, A.I.; Tsonos, C.; Assimakis, N. Automatic model-based tracing algorithm for vessel segmentation and diameter estimation. Comput. Methods Programs Biomed. 2010, 100, 108–122.
. Vlachos, M.; Dermatas, E. Multi-scale retinal vessel segmentation using line tracking. Comput. Med.Imaging Graph. 2010, 34, 213–227.
. Sheng, B.; Li P.; Mo, S.; Li, H.; Hou, X.; Wu, Q.; Qin, J.; Fang, R.; Feng, D.D. Retinal Vessel Segmentation Using Minimum Spanning Superpixel Tree Detector. IEEE Trans. Cybern. 2018, 1–13, doi:10.1109/TCYB.2018.2833963.
. Espona, L.; Carreira, M.J.; Penedo, M.G.; Ortega, M. Retinal vessel tree segmentation using a deformable contour model. In Proceedings of the 2008 19th International Conference on Pattern Recognition, Tampa, FL, USA, 8–11 December 2008; pp. 1-4.
. Al-Diri, B.; Hunter, A.; Steel, D. An active contour model for segmenting and measuring retinal vessels. IEEE Trans. Med. Imaging 2009, 28, 1488–1497.
. Palomera-Pérez, M.A.; Martinez-Perez, M.E.; Benítez-Pérez, H.; Ortega-Arjona, J.L. Parallel multiscale feature extraction and region growing: Application in retinal blood vessel detection. IEEE Trans. Inf. Technol. Biomed. 2010, 14, 500–506.
. Salazar-Gonzalez, A.; Kaba, D.; Li, Y.; Liu, X. Segmentation of the blood vessels and optic disk in retinal images. IEEE J. Biomed. Health Inform. 2014, 18, 1874–1886.
. Zhao, Y.; Rada, L.; Chen, K.; Harding, S.P.; Zheng, Y. Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images. IEEE Trans. Med. Imaging 2015, 34, 1797–807.
. Gao, X.; Cai, Y.; Qiu, C.; Cui, Y. Retinal blood vessel segmentation based on the Gaussian matched filter and U-net. In Proceedings of the 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China, 14–16 October 2017; pp. 1–5.
. Li, M.; Yin, Q.; Lu, M. Retinal Blood Vessel Segmentation Based on Multi-Scale Deep Learning. In Proceedings of the 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China, 14–16 October 2017.
. Dasgupta, A.; Singh, S. A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. In Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, Australia, 18–21 April 2017; pp. 248–251.
. X. Wang, X. Jiang , J. Ren, ‘Blood vessel segmentation from fundus image by a cascade classification framework,’ Pattern Recognition 88 (2019) 331–341.
. D. A. Dharmawan and B. P. Ng, “A new two-dimensional matched filter based on the modified chebyshev type i function for retinal vessels detection,” in Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE. IEEE, 2017, pp. 369–372.
. X. Gao, Y. Cai, C. Qiu, and Y. Cui, “Retinal blood vessel segmentation based on the gaussian matched filter and u-net,” in Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017 10th International Congress on. IEEE, 2017, pp. 1–5.
. Staal J, Abràmoff MD, Niemeijer M, Viergever MA, Van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23:501–509
. Hoover A, Goldbaum M (2003) Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans Med Imaging 22:951–958
. Gerig, Guido, et al. "Nonlinear anisotropic filtering of MRI data", IEEE Transactions on medical imaging 11.2 (1992), 221-232.
. A. Hoover, V. Kouznetsova and M. Goldbaum, "Locating Blood Vessels in Retinal Images by Piece-wise Threhsold Probing of a Matched Filter Response", IEEE Transactions on Medical Imaging , vol. 19 no. 3, pp. 203-210, March 2000
. B. Lam , Y.G.A. Liew , General retinal vessel segmentation using regulariza- tion-based multiconcavity modeling, IEEE Trans. Med. Imaging 29 (7) (2010) 1369–1381.
. Y. Wang , G. Ji , P. Lin , E. Trucco , Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition, Pattern Recognit. 46 (8) (2013) 2117–2133.
. U.T. Nguyen , A. Bhuiyan , L.A. Park , K. Ramamohanarao , An effective retinal blood vessel segmentation method using multi-scale line detection, Pattern Recognit. 46 (3) (2013) 703–715.
. Y.Q. Zhao , X.H. Wang , X.F. Wang , F.Y. Shih , Retinal vessels segmentation based on level set and region growing, Pattern Recognit. 47 (7) (2014) 2437–2446.
. B. Yin , H. Li , B. Sheng , X. Hou , Y. Chen , W. Wu , P. Li , R. Shen , Y. Bao , W. Jia , Vessel extraction from non-fluorescein fundus images using orientation-aware detector, Med. Image Anal. 26 (1) (2015) 232–242.
. G. Azzopardi , N. Strisciuglio , M. Vento , N. Petkov , Trainable cosfire filters for vessel delineation with application to retinal images, Med. Image Anal. 19 (1) (2015) 46–57.
. J. Zhang , B. Dashtbozorg , E. Bekkers , J. Pluim , R. Duits , B. ter Haar Romeny ,Ro- bust retinal vessel segmentation via locally adaptive derivative frames in ori- entation scores, IEEE Trans. Med. Imaging 35 (12) (2016) 2631–2644.
. K. Rezaee , J. Haddadnia , A. Tashk , Optimized clinical segmentation of retinal blood vessels by using combination of adaptive filtering, fuzzy entropy and skeletonization, Appl. Soft Comput. 52 (2017) 937–951.
. X. You , Q. Peng , Y. Yuan , Y.M. Cheung , J. Lei ,Segmentation of retinal blood ves- sels using the radial projection and semi-supervised approach, Pattern Recog- nit. 44 (10) (2011) 2314–2324.
. 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, IEEE Trans. Med. Imaging 30 (1) (2011) 146–158.
. M.M. Fraz , P. Remagnino , A. Hoppe , B. Uyyanonvara , A.R. Rudnicka , C.G. Owen , S. Barman , An ensemble classification-based approach applied to retinal blood vessel segmentation, IEEE Trans. Biomed. Eng. 59 (9) (2012) 2538–2548.
. S. Roychowdhury , D. Koozekanani , K. Parhi , Blood vessel segmentation of fun- dus images by major vessel extraction and sub-image classification, IEEE J. Biomed. Health Inform. 19 (3) (2015) 1118–1128.
. R. Vega , G. Sanchez-Ante , L.E. Falcon-Morales , H. Sossa , E. Guevara , Retinal ves- sel extraction using lattice neural networks with dendritic processing, Comput. Biol. Med. 58 (2015) 20–30.
. Q. Li , B. Feng , L. Xie , P. Liang , H. Zhang , T. Wang , A cross-modality learning approach for vessel segmentation in retinal images, IEEE Trans. Med. Imaging 35 (1) (2016) 109–118.
. P. Liskowski , K. Krawiec , Segmenting retinal blood vessels with deep neural networks, IEEE Trans. Med. Imaging 35 (11) (2016) 2369–2380.
. B. Barkana , I. Saricicek , B. Yildirim , Performance analysis of descriptive statisti- cal features in retinal vessel segmentation via fuzzy logic, ann, svm, and clas- sifier fusion, Knowl. Based Syst. 118 (2017) 165–176.
. J. Zhang , Y. Chen , E. Bekkers , M. Wang , B. Dashtbozorg , B. ter Haar Romeny , Retinal vessel delineation using a brain-inspired wavelet transform and ran- dom forest, Pattern Recognit. 69 (2017) 107–123.
. C. Wang, Z. Zhao, Q. Ren, Y. Xu, and Y. Yu, “Dense u-net based on patch-based learning for retinal vessel segmentation,” Entropy, vol. 21, no. 2, p. 168, 2019.
. T. A. Soomro, A. J. Afifi, J. Gao, O. Hellwich, L. Zheng, and M. Paul, “Strided fully convolutional neural network for boosting the sensitivity of retinal blood vessels segmentation,” Expert Systems with Applications, vol. 134, pp. 36–52, 2019.
Copyright (c) 2020 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who submit papers with this journal agree to the following terms:
- Authors retain copyright and grant the journal 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) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
- By submitting the processing fee, it is understood that the author has agreed to our terms and conditions which may change from time to time without any notice.
- It should be clear for authors that the Editor In Chief is responsible for the final decision about the submitted papers; have the right to accept\reject any paper. The Editor In Chief will choose any option from the following to review the submitted papers:A. send the paper to two reviewers, if the results were negative by one reviewer and positive by the other one; then the editor may send the paper for third reviewer or he take immediately the final decision by accepting\rejecting the paper. The Editor In Chief will ask the selected reviewers to present the results within 7 working days, if they were unable to complete the review within the agreed period then the editor have the right to resend the papers for new reviewers using the same procedure. If the Editor In Chief was not able to find suitable reviewers for certain papers then he have the right to accept\reject the paper.B. sends the paper to a selected editorial board member(s). C. the Editor In Chief himself evaluates the paper.
- Author will take the responsibility what so ever if any copyright infringement or any other violation of any law is done by publishing the research work by the author
- Before publishing, author must check whether this journal is accepted by his employer, or any authority he intends to submit his research work. we will not be responsible in this matter.
- If at any time, due to any legal reason, if the journal stops accepting manuscripts or could not publish already accepted manuscripts, we will have the right to cancel all or any one of the manuscripts without any compensation or returning back any kind of processing cost.
- The cost covered in the publication fees is only for online publication of a single manuscript.