Journal of Engineering and Applied Sciences

Year: 2019
Volume: 14
Issue: 16
Page No. 5794 - 5799

Segmented Retinal Blood Vessels of Healthy and Diabetic Retinopathy Individual

Authors : Akande Noah Oluwatobi, Abikoye Oluwakemi Christianah, Ayegba Peace, Gbadamosi Babatunde and Adegun Adekanmi Adeyinka

References

Akram, M.U., A. Tariq, S.A. Khan and M.Y. Javed, 2014. Automated detection of exudates and macula for grading of diabetic macular edema. Comput. Methods Programs Biomed., 114: 141-152.
CrossRef  |  PubMed  |  Direct Link  |  

Amin, J., M. Sharif and M. Yasmin, 2016. A review on recent developments for detection of diabetic retinopathy. Sci., 2016: 1-20.
CrossRef  |  PubMed  |  Direct Link  |  

Banerjee, S. and D. Kayal, 2016. Detection of hard exudates using mean shift and normalized cut method. Biocybernetics Biomed. Eng., 36: 679-685.
CrossRef  |  Direct Link  |  

Barkana, B.D., I. Saricicek and B. Yildirim, 2017. Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM and classifier fusion. Knowl. Based Syst., 118: 165-176.
CrossRef  |  Direct Link  |  

Bhargavi, R.V., R.K. Senapati, G. Swain and P.M.K. Prasad, 2016. Computerized diabetic patients fundus image screening for lesion regions detection and grading. Biomed. Res. India, 27: S443-S449.
Direct Link  |  

Franklin, S.W. and S.E. Rajan, 2014. Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images. Biocybernetics Biomed. Eng., 34: 117-124.
CrossRef  |  Direct Link  |  

Franklin, S.W. and S.E. Rajan, 2014. Retinal vessel segmentation employing ANN technique by Gabor and moment invariants-based features. Appl. Soft Comput., 22: 94-100.
CrossRef  |  Direct Link  |  

Fraz, M.M., P. Remagnino, A. Hoppe, B. Uyyanonvara and A.R. Rudnicka et al., 2012. Blood vessel segmentation methodologies in retinal images-a survey. Comput. Methods Programs Biomed., 108: 407-433.
CrossRef  |  PubMed  |  Direct Link  |  

Guedri, H., M.B. Abdallah, F. Echouchene and H. Belmabrouk, 2017. Novel computerized method for measurement of retinal vessel diameters. Biomedicines, 5: 1-17.
CrossRef  |  PubMed  |  Direct Link  |  

Imani, E., M. Javidi and H.R. Pourreza, 2015. Improvement of retinal blood vessel detection using morphological component analysis. Comput. Methods Programs biomed., 118: 263-279.
CrossRef  |  PubMed  |  Direct Link  |  

Kurilova, V., J. Pavlovicova, M. Oravec, R. Rakar and I. Marcek, 2015. Retinal blood vessels extraction using morphological operations. Proceedings of the 2015 International Conference on Systems, Signals and Image Processing (IWSSIP), September 10-12, 2015, IEEE, London, UK., ISBN:978-1-4673-8353-0, pp: 265-268.

Li, X. and W.G. Wee, 2014. Retinal vessel detection and measurement for computer-aided medical diagnosis. J. Digital Imag., 27: 120-132.
CrossRef  |  Direct Link  |  

Mahendran, G. and R. Dhanasekaran, 2015. Investigation of the severity level of diabetic retinopathy using supervised classifier algorithms. Comput. Electr. Eng., 45: 312-323.
CrossRef  |  Direct Link  |  

Nguyen, U.T.V., A. Bhuiyan, L.A. Park and K. Ramamohanarao, 2013. An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recognit., 46: 703-715.
CrossRef  |  Direct Link  |  

Prentasic, P., S. Loncaric, Z. Vatavuk, G. Bencic and M. Subasic et al., 2013. Diabetic retinopathy image database (DRiDB): A new database for diabetic retinopathy screening programs research. Proceedings of the 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), September 4-6, 2013, IEEE, Trieste, Italy, pp: 711-716.

Ramani, R.G. and L. Balasubramanian, 2016. Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis. Biocybernet. Biomed. Eng., 36: 102-118.
CrossRef  |  Direct Link  |  

Sigurosson, E.M., S. Valero, J.A. Benediktsson, J. Chanussot and H. Talbot et al., 2014. Automatic retinal vessel extraction based on directional mathematical morphology and fuzzy classification. Pattern Recognit. Lett., 47: 164-171.
Direct Link  |  

Srividhyaa, R. and V. Sumalatha, 2017. A graph based segmentation for extraction of blood vessels in retinal images. Intl. J. Pure Appl. Math., 116: 675-684.
Direct Link  |  

Valverde, C., M. Garcia, R. Hornero and M.I. Lopez-Galvez, 2016. Automated detection of diabetic retinopathy in retinal images. Indian J. Ophthalmol., 64: 26-32.
CrossRef  |  PubMed  |  Direct Link  |  

Wang, S., Y. Yin, G. Cao, B. Wei and Y. Zheng et al., 2015. Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing, 149: 708-717.
CrossRef  |  Direct Link  |  

Welikala, R.A., J. Dehmeshki, A. Hoppe, V. Tah and S. Mann et al., 2014. Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification. Comput. Methods Programs Biomed., 114: 247-261.
CrossRef  |  PubMed  |  Direct Link  |  

Yavuz, Z. and C. Kose, 2017. Blood vessel extraction in color retinal fundus images with enhancement filtering and unsupervised classification. J. Healthcare Eng., 2017: 1-12.
CrossRef  |  Direct Link  |  

Zhu, C., B. Zou, R. Zhao, J.Cui, X. Duan, Z. Chen and Y. Liang, 2017. Retinal vessel segmentation in colour fundus images using extreme learning machine. Comput. Med. Imag. Graphics, 55: 68-77.
CrossRef  |  Direct Link  |  

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