A Review on Methods of Enhancement And Denoising in Retinal Fundus Images
DOI:
https://doi.org/10.26438/ijcse/v8i12.19Keywords:
Spatial Domain filtering;, Contrast EnhancementAbstract
Diabetic Retinopathy (DR) is a disease caused by abnormalities in blood vessels in the eyes. DR can be detected in the early stages by the Detection of Micro Aneurysms in fundus retinal images. Retinal fundus pictures are commonly used for finding and analysis of DR disease that help ophthalmologists to complete the evaluation of retinal diseases. By reduction in noise level and by enhancing some features in the image pre-processing techniques are adopted. Restoration of images is done to happen by numerous pre-processing techniques. Here in this paper, the comparison of pre- processing in the retinal fundus image is done. For the precise visual view of DR-related highlights, the nature of fundus pictures should be enhanced to a satisfactory level. The difference is a more critical quality than a unique degree of splendor and goals. The main purpose of the pre-processing technique is to increase the diagnostic possibility in fundus images for visual assessment and also for computer-aided segmentation. This paper deals with the comparison of different retinal image denoising technique and their parameters such as MSE, PSNR, Correlation coefficient, RMS values, etc were reviewed and compared with different datasets for retinal images in connection with the identification of DR and Micro Aneurysms (MA).
References
[1] Sisodia, D. S, Nair S, & Khobragade, “Diabetic retinal fundus images Preprocessing and feature extraction for early detection of Diabetic Retinopathy”, Biomedical and Pharmacology Journal, Vol.10, Issue.2, pp.615-626, 2017.
[2] Salazar-Gonzalez, A., Kaba, D., Li, Y., & Liu, X. “Segmentation of the blood vessels and optic disk in retinal images“. IEEE Journal of biomedical and health informatics, Vol.18, Issue.6, pp.1874-1886, 2014.
[3] Lazar, I., & Hajdu, A. “Retinal microaneurysm detection through local rotating cross-section profile analysis“. IEEE transactions on medical imaging, Vol.32, No.2, pp.400-407, 2012.
[4] Shetty, P. G., Patil, S. A., & Avadhoot, R. T. “Detection of Microaneurysm and Diabetic Retinopathy Grading in Fundus Retinal Images“. International Journal of Engineering Trends and Technology, Vol.13, No.7, pp.331-336. 2014.
[5] Kajal Patel, Yogesh Kumar, “Glaucoma detection and classification: A Review“. International journal of computer science and engineering. Vol.7, Issue.4, pp 543-547, 2019.
[6] Gokilavani, C., Rajeswaran, N., Karthick, V. J. A., Kumar, R. S., & Thangadurai, N. “Comparative results performance analysis of various filters used to remove noises in retinal images“. Online International Conference on Green Engineering and Technologies, India 2015.
[7] Qureshi, I., Ma, J., & Shaheed, K. “A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy. Algorithms“, Vol.12 No.1, pp. 14, 2019.
[8] Saurabh, Gaurav, “Survey of automatic detection of diabetic retinopathy using digital image processing“. International journal of computer science and engineering. Vol.7, Issue.3, pp 352-355, 2019.
[9] Yavuz, Z., & Köse, C.“Blood vessel extraction in color retinal fundus images with enhancement filtering and unsupervised classification“ Journal of healthcare engineering, Vol.8, Issue.3, 2017.
[10] Rathinam, S., & Selvarajan, S. “Comparison of image preprocessing techniques on fundus images for early diagnosis of glaucoma“. Int J Sci Eng Res, Vol. 4, pp. 1368-1372, 2013.
[11] Zohair, A. A., Shamil, A. A., & Sulong, G. “Latest methods of image enhancement and restoration for computed tomography: a concise review“. Applied Medical Informatics, Vol.36, Issue.1, pp. 1-12. 2015.
[12] Abbas, Q.; Fondon, I.; Sarmiento, A.; Jiménez, S.; Alemany, P.J.M. “Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features“. Med. Biol. Eng. Comput., Vol.55, pp. 1959–1974. 2017
[13] Kumar, S., Kumar, P., Gupta, M., & Nagawat, A. K. (2010). "Performance comparison of median and wiener filter in image de-noising". International Journal of Computer Applications, Vol.12, Issue.4, pp. 27-31, 2010
[14] Vijayalakshmi, A., Titus, C., & Beaulah, H. L. "Image Denoising for different noise models by various filters: A Brief Survey". International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Vol.3, Issue.6, pp. 42-45, 2014.
[15] Qureshi, I., Ma, J., & Abbas, Q. (2019). "Recent development on detection methods for the diagnosis of diabetic retinopathy". Symmetry, Vol.11, No.6, pp. 749. 2019
[16] Fan, L., Zhang, F., Fan, H., & Zhang, C. (2019). "Brief review of image denoising techniques". Visual Computing for Industry, Biomedicine, and Art, Vol.2, No.1, pp. 7-10, 2019.
[17] Borges, V. R. P., dos Santos, D. J., Popovic, B., & Cordeiro, D. F. "Segmentation of blood vessels in retinal images based on nonlinear filtering". In 2015 IEEE 28th International Symposium on Computer-Based Medical Systems IEEE. pp. 95-96, June 2015
[18] Singla, N., & Singh, N. (2017). "Blood Vessel Contrast Enhancement Techniques for Retinal Images". International Journal of Advanced Research in Computer Science, Vol.8, No.5, 2017.
[19] Soorya, M., Issac, A., & Dutta, M. K. "An automated and robust image processing algorithm for glaucoma diagnosis from fundus images using novel blood vessel tracking and bend point detection". International journal of medical informatics, Vol.110, pp. 52-70, 2018.
[20] Elseid, A. A. G., Elmanna, M. E., & Hamza, A. O. "Evaluation of spatial filtering techniques in retinal fundus images". American Journal of Artificial Intelligence, Vol.2, Issue.2, pp. 16, 2018
[21] Jebaseeli, T. J., Durai, C. A. D., & Peter, J. D. "Retinal blood vessel segmentation from diabetic retinopathy images using tandem PCNN model and deep learning-based SVM". Optik, Vol.199, pp.163328, 2018.
[22] Kumar, H. V., Jayaram, A., Karegowda, A., & Bharathi, P. "A comparative study on filters with special reference to retinal images." Proc Int J Comput Appl, Vol.138, No. (5), pp.81-6, 2016.
[23] Siva Sundhara Raja, D., & Vasuki, S. "Automatic detection of blood vessels in retinal images for diabetic retinopathy diagnosis." Computational and mathematical methods in medicine, 2015.
[24] Lestari, T., & Luthfi, A. "Retinal Blood Vessel Segmentation using Gaussian Filter". In Journal of Physics: Conference Series. IOP Publishing. Vol.1376, No. 1, pp. 012023, November 2019.
[25] Rasta, S. H., Partovi, M. E., Seyedarabi, H., & Javadzadeh, A. "A comparative study on preprocessing techniques in diabetic retinopathy retinal images: illumination correction and contrast enhancement". Journal of Medical signals and sensors, Vol.5, Issue.1, pp. 40, 2015.
[26] Al-amri, S. S., Kalyankar, N. V., & Khamitkar, S. D. "Linear and non-linear contrast enhancement image." International Journal of Computer Science and Network Security, Vol.10(2), pp.139-143, 2010
[27] Singh, K., & Kapoor, R. " Image enhancement using exposure-based sub-image histogram equalization". Pattern Recognition Letters, Vol.36, pp.10-14, 2014.
[28] Rampal, H., Kumar, R. K., Ramanathan, B., & Das, T. P. "Complex shock filtering applied to retinal image enhancement". In World Congress on Medical Physics and Biomedical Engineering, Beijing, China. Springer, Berlin, Heidelberg. Vol.26, No.31, pp. 900-903, May 2012
[29] Azzopardi, G., & Petkov, N. "Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters". Pattern Recognition Letters, Vol.34(8), pp.922-933, 2013.
[30] Chakraborti, T., Jha, D. K., Chowdhury, A. S., & Jiang, X. (2015). "A self-adaptive matched filter for retinal blood vessel detection". Machine Vision and Applications, Vol.26, Issue.1, pp.55-68, 2015
[31] Miri, M., Amini, Z., Rabbani, H., & Kafieh, R. "A comprehensive study of retinal vessel classification methods in fundus images". Journal of medical signals and sensors, Vol.7, Issue.2, pp. 59, 2017.
[32] Kande, G. B., Subbaiah, P. V., & Savithri, T. S."Unsupervised fuzzy-based vessel segmentation in pathological digital fundus images". Journal of medical systems, Vol.34, Issue.5, pp.849-858, 2010.
[33] Kondermann, C., Kondermann, D., & Yan, M. "Blood vessel classification into arteries and veins in retinal images". In Medical Imaging 2007: Image Processing International Society for Optics and Photonics. Vol. 6512, pp. 651247, March 2007.
[34] Kande, G. B., Savithri, T. S., & Subbaiah, P. V. "Automatic detection of microaneurysms and hemorrhages in digital fundus images". Journal of digital imaging, Vol.23, Issue.4, pp.430-437, 2010.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
