UPSI Digital Repository (UDRep)
|
UPSI Digital Repository (UDRep)
|
|
|
| ||||||||||||||||||||||
| Abstract : Universiti Pendidikan Sultan Idris |
| Optometrists, ophthalmologists, orthoptists, and other trained medical professionals use fundus photography to monitor the progression of certain eye conditions or diseases. Segmentation of the vessel tree is an essential process of retinal analysis. In this paper, an interactive blood vessel segmentation from retinal fundus image based on Canny edge detection is proposed. Semi-automated segmentation of specific vessels can be done by simply moving the cursor across a particular vessel. The pre-processing stage includes the green color channel extraction, applying Contrast Limited Adaptive Histogram Equalization (CLAHE), and retinal outline removal. After that, the edge detection techniques, which are based on the Canny algorithm, will be applied. The vessels will be selected interactively on the developed graphical user interface (GUI). The program will draw out the vessel edges. After that, those vessel edges will be segmented to bring focus on its details or detect the abnormal vessel. This proposed approach is useful because different edge detection parameter settings can be applied to the same image to highlight particular vessels for analysis or presentation. |
| References |
Abramoff, M. D., Garvin, M. K., & Sonka, M. (2010). Retinal imaging and image analysis. IEEE Reviews in Biomedical Engineering, 3, 169-208. doi:10.1109/RBME.2010.2084567 Almotiri, J., Elleithy, K., & Elleithy, A. (2018). Retinal vessels segmentation techniques and algorithms: A survey. Applied Sciences (Switzerland), 8(2) doi:10.3390/app8020155 Alwazzan, M. J., Ismael, M. A., & Ahmed, A. N. (2021). A hybrid algorithm to enhance colour retinal fundus images using a wiener filter and CLAHE. Journal of Digital Imaging, 34(3), 750-759. doi:10.1007/s10278-021-00447-0 Ansari, M. A., Kurchaniya, D., & Dixit, M. (2017). A comprehensive analysis of image edge detection techniques. International Journal of Multimedia and Ubiquitous Engineering, 12(11), 1-12. Retrieved from www.scopus.com Armaly, M. F. (1970). Optic cup in normal and glaucomatous eyes. Investigative Ophthalmology, 9(6), 425-429. Retrieved from www.scopus.com Aurangzeb, K., Aslam, S., Alhussein, M., Naqvi, R. A., Arsalan, M., & Haider, S. I. (2021). Contrast enhancement of fundus images by employing modified PSO for improving the performance of deep learning models. IEEE Access, 9, 47930-47945. doi:10.1109/ACCESS.2021.3068477 Besenczi, R., Tóth, J., & Hajdu, A. (2016). A review on automatic analysis techniques for color fundus photographs. Computational and Structural Biotechnology Journal, 14, 371-384. doi:10.1016/j.csbj.2016.10.001 Beutel, J., Kundel, H. L., & Van Metter, R. L. (2000). Handbook of Medical Imaging, Retrieved from www.scopus.com Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), 679-698. doi:10.1109/TPAMI.1986.4767851 Chakraborty, S., Chatterjee, S., Dey, N., Ashour, A. S., & Shi, F. (2017). Gradient approximation in retinal blood vessel segmentation. Paper presented at the 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics, UPCON 2017, , 2018-January 618-623. doi:10.1109/UPCON.2017.8251120 Retrieved from www.scopus.com Chang, C. -., Lin, C. -., Pai, P. -., & Chen, Y. -. (2009). A novel retinal blood vessel segmentation method based on line operator and edge detector. Paper presented at the IIH-MSP 2009 - 2009 5th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 299-302. doi:10.1109/IIH-MSP.2009.232 Retrieved from www.scopus.com Chang, S. H., Gong, L., Li, M., Hu, X., & Yan, J. (2008). Small retinal vessel extraction using modified canny edge detection. Paper presented at the ICALIP 2008 - 2008 International Conference on Audio, Language and Image Processing, Proceedings, 1255-1259. doi:10.1109/ICALIP.2008.4590140 Retrieved from www.scopus.com Chatterjee, S., Suman, A., Gaurav, R., Banerjee, S., Singh, A. K., Ghosh, B. K., . . . Maji, D. (2021). Retinal blood vessel segmentation using edge detection method. Paper presented at the Journal of Physics: Conference Series, , 1717(1) doi:10.1088/1742-6596/1717/1/012008 Retrieved from www.scopus.com Constantinou, M., Ferraro, J. G., Lamoureux, E. L., & Taylor, H. R. (2005). Assessment of optic disc cupping with digital fundus photographs. American Journal of Ophthalmology, 140(3), 529-531. doi:10.1016/j.ajo.2005.03.002 De Boever, P., Louwies, T., Provost, E., Int Panis, L., & Nawrot, T. S. (2014). Fundus photography as a convenient tool to study microvascular responses to cardiovascular disease risk factors in epidemiological studies. Journal of Visualized Experiments, (92) doi:10.3791/51904 Deng, G., & Cahill, L. W. (1994). Adaptive gaussian filter for noise reduction and edge detection. Paper presented at the IEEE Nuclear Science Symposium & Medical Imaging Conference, (pt 3) 1615-1619. Retrieved from www.scopus.com dos Santos, J. C. M., Carrijo, G. A., de Fátima dos Santos Cardoso,C., Ferreira, J. C., Sousa, P. M., & Patrocínio, A. C. (2020). Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and wiener filter. Research on Biomedical Engineering, 36(2), 107-119. doi:10.1007/s42600-020-00046-y Hoover, A. (2000). Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging, 19(3), 203-210. doi:10.1109/42.845178 Hoover, A. (0000). Structured analysis of the retina project website. Structured Analysis of the Retina, Retrieved from www.scopus.com Imran, A., Li, J., Pei, Y., Yang, J. -., & Wang, Q. (2019). Comparative analysis of vessel segmentation techniques in retinal images. IEEE Access, 7, 114862-114887. doi:10.1109/ACCESS.2019.2935912 Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision, 1(4), 321-331. doi:10.1007/BF00133570 Ketcham, D. J., Lowe, R. W., & Weber, J. W. (1974). Image enhancement techniques for cockpit displays. Image Enhancement Techniques for Cockpit Displays, Retrieved from www.scopus.com Khanamiri, H. N., Nakatsuka, A., & El-Annan, J. (2017). Smartphone fundus photography. Journal of Visualized Experiments, 2017(125) doi:10.3791/55958 Lee, G. -., Park, K. -., Oh, S. Y., & Kong, D. -. (2020). Analysis of optic chiasmal compression caused by brain tumors using optical coherence tomography angiography. Scientific Reports, 10(1) doi:10.1038/s41598-020-59158-1 Liang, Y., Qu, F., Chang, H., & Lu, W. (2012). The application study on the improved canny algorithm for edge detection in fundus image doi:10.1007/978-3-642-29455-6_80 Retrieved from www.scopus.com Mittal, M., Verma, A., Kaur, I., Kaur, B., Sharma, M., Goyal, L. M., . . . Kim, T. -. (2019). An efficient edge detection approach to provide better edge connectivity for image analysis. IEEE Access, 7, 33240-33255. doi:10.1109/ACCESS.2019.2902579 Mortensen, E. N., & Barrett, W. A. (1995). Intelligent scissors for image composition. Paper presented at the Proceedings of the ACM SIGGRAPH Conference on Computer Graphics, 191-198. doi:10.1145/218380.218442 Retrieved from www.scopus.com Muthukrishnan, R., & Radha, M. (2011). Edge detection techniques for image segmentation. International Journal of Computer Science & Information Technology (IJCSIT), 3(6), 259-267. Retrieved from www.scopus.com Ng, E. Y. K., Rajendra Acharya, U., Rangayyan, R. M., & Suri, J. S. (2014). Ophthalmological imaging and applications. Ophthalmological imaging and applications (pp. 1-500) doi:10.1201/b17026 Retrieved from www.scopus.com Paquet-Durand, F., Beck, S. C., Das, S., Huber, G., Le Chang, Schubert, T., . . . Seeliger, M. W. (2019). A retinal model of cerebral malaria. Scientific Reports, 9(1) doi:10.1038/s41598-019-39143-z Patwari, M. B., Manza, R. R., Rajput, Y. M., Saswade, M., & Deshpande, N. (2013). Detection and counting the microaneurysms using image processing techniques. International Journal of Applied Information Systems (IJAIS), 6(5), 11-17. Retrieved from www.scopus.com Pizer, S. M., Amburn, E. P., Austin, J. D., Cromartie, R., Geselowitz, A., Greer, T., . . . Zuiderveld, K. (1987). ADAPTIVE HISTOGRAM EQUALIZATION AND ITS VARIATIONS. Computer Vision, Graphics, and Image Processing, 39(3), 355-368. doi:10.1016/S0734-189X(87)80186-X Pratt, W. K. (1991). Digital Image Processing, Retrieved from www.scopus.com Robertson, G., Fleming, A., Williams, M. C., Trucco, E., Quinn, N., Hogg, R., . . . MacGillivray, T. J. (2020). Association between hypertension and retinal vascular features in ultra-widefield fundus imaging. Open Heart, 7(1) doi:10.1136/openhrt-2019-001124 Saleh, M. D., Eswaran, C., & Mueen, A. (2011). An automated blood vessel segmentation algorithm using histogram equalization and automatic threshold selection. Journal of Digital Imaging, 24(4), 564-572. doi:10.1007/s10278-010-9302-9 Singh, N., & Kaur, L. (2015). A survey on blood vessel segmentation methods in retinal images. Paper presented at the 2015 International Conference on Electronic Design, Computer Networks and Automated Verification, EDCAV 2015, 23-28. doi:10.1109/EDCAV.2015.7060532 Retrieved from www.scopus.com Sisodia, D. S., Nair, S., & Khobragade, P. (2017). Diabetic retinal fundus images: Preprocessing and feature extraction for early detection of diabetic retinopathy. Biomedical and Pharmacology Journal, 10(2), 615-626. doi:10.13005/bpj/1148 Staurenghi, G., Bottoni, F., & Giani, A. (2012). Clinical applications of diagnostic indocyanine green angiography. Retina fifth edition (pp. 51-81) doi:10.1016/B978-1-4557-0737-9.00002-3 Retrieved from www.scopus.com Tan, C. S., Ting, D. S., & Lim, L. W. (2019). Multicolor fundus imaging of polypoidal choroidal vasculopathy. Ophthalmology Retina, 3(5), 400-409. doi:10.1016/j.oret.2019.01.009 Tan, C. S. H., Chew, M. C. Y., Lim, L. W. Y., & Sadda, S. R. (2016). Advances in retinal imaging for diabetic retinopathy and diabetic macular edema. Indian Journal of Ophthalmology, 64(1), 76-83. doi:10.4103/0301-4738.178145 Tariq, N., Hamzah, R. A., Ng, T. F., Wang, S. L., & Ibrahim, H. (2021). Quality assessment methods to evaluate the performance of edge detection algorithms for digital image: A systematic literature review. IEEE Access, 9, 87763-87776. doi:10.1109/ACCESS.2021.3089210 Tavakoli, M., Nazar, M., & Mehdizadeh, A. (2020). The efficacy of microaneurysms detection with and without vessel segmentation in color retinal images. Paper presented at the Progress in Biomedical Optics and Imaging - Proceedings of SPIE, , 11314 doi:10.1117/12.2548527 Retrieved from www.scopus.com Venkatesh, P., Sharma, R., Vashist, N., Vohra, R., & Garg, S. (2015). Detection of retinal lesions in diabetic retinopathy: Comparative evaluation of 7-field digital color photography versus red-free photography. International Ophthalmology, 35(5), 635-640. doi:10.1007/s10792-012-9620-7 Vilela, M. A. P., Valença, F. M., Barreto, P. K. M., Amaral, C. E. V., & Pellanda, L. C. (2018). Agreement between retinal images obtained via smartphones and images obtained with retinal cameras or fundoscopic exams – systematic review and meta-analysis. Clinical Ophthalmology, 12, 2581-2589. doi:10.2147/OPTH.S182022 Xiao, Z., Zou, Y., & Wang, Z. (2020). An improved dynamic double threshold canny edge detection algorithm. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, , 11430 doi:10.1117/12.2539300 Retrieved from www.scopus.com |
| This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |