Using modified time-frequency algorithms for retinal vessel detection to diagnose eye diseases
Subject Areas : journal of Artificial Intelligence in Electrical Engineering
1 - Department of Electrical Engineering, AH.C., Islamic Azad University, Ahar, Iran
Keywords: retinal vessels, eye diseases, Gabor filter, sharpening algorithm, image processing.,
Abstract :
In medical sciences, detecting blood vessels in retinal images provides important information for ophthalmologists. The detection of vessels in retinal images is highly significant due to their critical role in eye health evaluation. These vessels are essential for diagnosing diseases, monitoring changes over time, and assessing conditions such as diabetic retinopathy, retinal artery occlusion, and ocular hypertension. To this end, this paper proposes a new algorithm for retinal vessel segmentation. The algorithm utilizes various image processing techniques, especially morphological processing. The innovative aspects of this research include using grayscale images obtained from all three channels of the color image, combining multiple image processing methods, applying Gabor filtering, and proposing a sharpening technique to enhance edges. Additionally, a modified method for background estimation and removal, as well as the use of morphological reconstruction for the final reconstruction and extraction of retinal vessels, are among the research objectives. The efficiency of the proposed algorithms has been evaluated in terms of accuracy, sensitivity, and specificity on the DRIVE and STARE datasets, and the results have been analyzed.
[1] M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, et al., "Blood vessel segmentation methodologies in retinal images–A survey," Computer methods and programs in biomedicine, vol. 108, pp. 407-433, 2012.
[2] S. A. Salem, N. M. Salem, and A. K. Nandi, "Segmentation of retinal blood vessels using a novel clustering algorithm (RACAL) with a partial supervision strategy," Medical & biological engineering & computing, vol. 45, pp. 261-273, 2007.
[3] K. Aurangzeb, R. S. Alharthi, S. I. Haider and M. Alhussein, "An Efficient and Light Weight Deep Learning Model for Accurate Retinal Vessels Segmentation," in IEEE Access, vol. 11, pp. 23107-23118, 2023.
[4] W. T. Freeman and E. H. Adelson, "The design and use of steerable filters," IEEE Transactions on Pattern analysis and machine intelligence, vol. 13, pp. 891-906, 1991.
[5] Z. H. Nasiruddin, W. M. Diyana W Zaki, S. A. Hudaibah and A. H. Nur Asyiqin, "Automated Retinal Blood Vessel Feature Extraction in Digital Fundus Images," 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Kota Kinabalu, Malaysia, 2022.
[6] M. Al-Rawi, M. Qutaishat, and M. Arrar, "An improved matched filter for blood vessel detection of digital retinal images," Computers in Biology and Medicine, vol. 37, pp. 262-267, 2007.
[7] A. Desiani, Erwin, B. Suprihatin, F. Efriliyanti, M. Arhami and E. Setyaningsih, "VG-DropDNet a Robust Architecture for Blood Vessels Segmentation on Retinal Image," in IEEE Access, vol. 10, pp. 92067-92083, 2022.
[8] M. D. Saleh, C. Eswaran, and A. Mueen, "An automated blood vessel segmentation algorithm using histogram equalization and automatic threshold selection," Journal of Digital Imaging, vol. 24, pp. 564-572, 2011.
[9] L. Espona, M. J. Carreira, M. Ortega, and M. G. Penedo, "A snake for retinal vessel segmentation," in Pattern Recognition and Image Analysis, ed: Springer, 2007, pp. 178-185.
[10] Walter T, Massin P, Ali Erginay A, Ordonez R , Jeulin C, Klein JC, "Automatic detection of microaneurysms in color fundus images," Med Image Analysis,11: 555–66, 2007.
[11] Porreza HR, Bahreyni Toossi MH, Mehdizadeh AR, Pourreza R, Tavakoli M, "Automated detection of microaneurysm in color fundus by using local radon transform," Iranian Journal of Medical Physics, 6(1):1320, 2009.
[12] E. Ricci and R. Perfetti, "Retinal blood vessel segmentation using line operators and support vector classification," Medical Imaging, IEEE Transactions on, vol. 26, pp. 1357-1365, 2007.
[13] A. Osareh and B. Shadgar, "Automatic blood vessel segmentation in color images of retina," Iran. J. Sci. Technol. Trans. B: Engineering, vol. 33, pp. 191-206, 2009.
[14] L. Xu and S. Luo, "A novel method for blood vessel detection from retinal images," Biomedical engineering online, vol. 9, p. 14, 2010.
[15] D. Marín, A. Aquino, M. E. Gegúndez-Arias, and 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 on, vol. 30, pp. 146-158, 2011.
[16] N. Dey, A. B. Roy, M. Pal, and A. Das, "FCM Based Blood Vessel Segmentation Method for Retinal Images," arXiv preprint arXiv:1209.1181, 2012.
[17] M. M. Fraz, S. Barman, P. Remagnino, A. Hoppe, A. Basit, B. Uyyanonvara, et al., "An approach to localize the retinal blood vessels using bit planes and centerline detection," Computer methods and programs in biomedicine, vol. 108, pp. 600-616, 2012.
[18] B. Dai, W. Bu, X. Wu, and Y. Teng, "Retinal vessel segmentation via Iterative Geodesic Time Transform," in Pattern Recognition (ICPR), 2012 21st International Conference on, 2012, pp. 561-564.
[19] Li W, Bhalerao A, Wilson R, ”Analysis of retinal vasculature using a multiresolution Hermite model”, IEEE Transactions on Medical Imaging. vol. 26, PP 137–152, 2007.
[20] Lupascu C.A, Tegolo D, Trucco E, ”FABC: retinal vessel segmentation using AdaBoost”, IEEE Transactions on Information Technology in Biomedicine, vol. 14 ,pp 1267–1274, 2010.
[21] Lam B.S.Y, Yongsheng G, Liew A.W.C, ”General retinal vessel segmentation using regularization-based multiconcavity modeling”, IEEE Transactions on Medical Imaging, vol. 29 ,PP 1369–1381, 2010.