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Retinal Vessel Segmentation to Support Foveal Avascular Zone Detection
Abstract
Purpose: This study aims to perform retinal vessel segmentation to support foveal avascular zone detection. Methodology: The proposed approach consists of a multi-stage image processing approach, including preprocessing, image quality enhancementt, and segmentation of retinal blood vessel using matched filter and length filter techniques.
Findings: The proposed framework has achieved remarkable results with an average sensitivity, specificity, and accuracy of 77.99%, 86.43%, and 85.24%, respectively.
Value: This achievement has the potential to significantly enhance the accuracy and efficiency of detecting and diagnosing medical conditions related to the retina, improving the quality of life for countless individuals.Full Text:
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W. Joan. Ho, Allen C. Miller, “Retina 2011: The Magical World of Retina,” Subspecialty day AAO 2010, pp. 150–155, 2011.
M. M. Fraz, P. Remagnino, A. Hoppe, S. Velastin, B. Uyyanonvara, and S. a. Barman, “A supervised method for retinal blood vessel segmentation using line strength, multiscale Gabor and morphological features,” IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 410–415, Nov. 2011, doi: 10.1109/ICSIPA.2011.6144129.
P. Liskowski and K. Krawiec, “Segmenting Retinal Blood Vessels With Deep Neural Networks,” IEEE Trans Med Imaging, vol. 35, no. 11, pp. 2369–2380, 2016, doi: 10.1109/TMI.2016.2546227.
B. Zhang, L. Zhang, L. Zhang, and F. Karray, “Retinal vessel extraction by matched filter with first-order derivative of Gaussian,” Comput Biol Med, vol. 40, no. 4, pp. 438–445, 2010, doi: 10.1016/j.compbiomed.2010.02.008.
S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters.,” IEEE Trans Med Imaging, vol. 8, no. 3, pp. 263–269, 1989, doi: 10.1109/42.34715.
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 Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2017, pp. 369–372. doi: 10.1109/EMBC.2017.8036839.
M. Al-Rawi, M. Qutaishat, and M. Arrar, “An improved matched filter for blood vessel detection of digital retinal images.,” Comput Biol Med, vol. 37, no. 2, pp. 262–267, 2007, doi: 10.1016/j.compbiomed.2006.03.003.
N. P. Singh and R. Srivastava, “Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter,” Comput Methods Programs Biomed, vol. 129, pp. 40–50, 2016.
D. A. Dharmawan, B. P. Ng, and S. Rahardja, “A Modified Dolph-Chebyshev Type II Function Matched Filter for Retinal Vessels Segmentation,” Symmetry (Basel), vol. 10, no. 7, p. 257, 2018.
H. Zolfagharnasab and A. R. Naghsh-nilchi, “Cauchy Based Matched Filter for Retinal Vessels Detection,” J Med Signals Sens, vol. 4, no. 1, pp. 1–9, 2014.
M. Nandy and M. Banrejee, “Retinal Vessel Segmentation Using Gabor Filter and Artificial Neural Network,” Third International Conference on Emerging Applications of Information Technology (EAIT), no. 4, pp. 157–160, 2012.
A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, “Multiscale vessel enhancement filtering,” in Medical Image Computing and Computer-Assisted Intervention --- MICCAI’98, W. M. Wells, A. Colchester, and S. Delp, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 1998, pp. 130–137.
D. A. Dharmawan, B. P. Ng, and N. Borijindargoon, “Design of Optimal Adaptive Filters for Two-Dimensional Filamentary Structures Segmentation,” IEEE Signal Process Lett, vol. 26, no. 10, pp. 1511–1515, 2019, doi: 10.1109/LSP.2019.2938631.
M. H. A. Fadzil, L. I. Izhar, and H. A. Nugroho, “Analysis of Foveal Avascular Zone for grading of Diabetic Retinopathy,” International Journal of Recent Trends in Engineering, vol. 6, no. 3, pp. 232–250, 2011, doi: 10.1504/IJBET.2011.041463.
M. Liao, Y. Q. Zhao, X. H. Wang, and P. S. Dai, “Retinal vessel enhancement based on multi-scale top-hat transformation and histogram fitting stretching,” Opt Laser Technol, vol. 58, pp. 56–62, 2014, doi: 10.1016/j.optlastec.2013.10.018.
DOI: https://doi.org/10.31315/telematika.v20i1.9645
DOI (PDF): https://doi.org/10.31315/telematika.v20i1.9645.g5399
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Status Kunjungan Jurnal Telematika