American Journal of Artificial Intelligence
Volume 2, Issue 2, December 2018, Pages: 16-21
Received: Sep. 24, 2018;
Accepted: Oct. 6, 2018;
Published: Oct. 27, 2018
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Arwa Ahmed Gasm Elseid, Department of Biomedical Engineering, Sudan University, Khartoum, Sudan
Mohamed Eltahir Elmanna, Department of Biomedical Engineering, Almughtaribeen University, Khartoum, Sudan
Alnazier Osman Hamza, Department of Radiology, Medical Sciences and Technology University, Khartoum, Sudan
The denoising of the fundus images is an essential pre-processing step in glaucoma diagnosis to ensure sufficient quality for the Computer Aided Diagnosing (CAD) system. In this paper, we present an evaluation approach for different denoising filters of eye fundus images that suffer from two different types of noises (Gaussian noise and Salt & Pepper noise), which had been applied to the retinal images and then various Spatial filtering techniques like linear (Gaussian, mean), nonlinear filtering (median) and adaptive filtering have been implemented to three types of images (original images, images with salt and pepper noise and images with Gaussian noise) and their performance are compared to each other based on evaluation parameters: Mean Squared Error (MSE), Peak Signal Noise Ratio (PSNR) and Structural Similarity (SSIM). The results showed that the adaptive median filter has the best performance in salt & paper noise and the adaptive filter has the best performance for Gaussian noise, but their performance is close to each other. In conclusion, six spatial filters applied to RIM-ONE fundus image database and found that, the adaptive median filter has the best performance compared to other filters to remove these noises and increase the quality of the resulting images, which can be implemented to the CAD system.
Arwa Ahmed Gasm Elseid,
Mohamed Eltahir Elmanna,
Alnazier Osman Hamza,
Evaluation of Spatial Filtering Techniques in Retinal Fundus Images, American Journal of Artificial Intelligence.
Vol. 2, No. 2,
2018, pp. 16-21.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Bandara, A. M. R. R., and P. W. G. R. M. P. B. Giragama. "A Retinal Image Enhancement Technique for Blood Vessel Segmentation Algorithm." Industrial and Information Systems (Iciis), 2017 Ieee International Conference On. Ieee, 2017.
Ba, Alper, and M. Emin Yüksel. "Impulse Noise Removal from Digital Images by a Detail-Preserving Filter Based On Type-2 Fuzzy Logic." Ieee Transactions On Fuzzy Systems 16.4 (2008): 920-928.
Mélange, Tom, Mike Nachtegael, and Etienne E. Kerre. "Fuzzy Random Impulse Noise Removal from Color Image Sequences." Ieee Transactions On Image Processing 20.4 (2011): 959-970.
Gupta, Sayantan, and Sukanya Roy. "Medav Filter—Filter For Removal Of Image Noise With The Combination Of Median And Average Filters." Recent Trends in Signal and Image Processing. Springer, Singapore, 2019. 11-19.
Mandar Sontakke, Meghana Kulkarni, “Different Types Of Noises In Images And Noise Removing Technique”, International Journal Of Advanced Technology In Engineering And Science, Volume No.03, Issue No. 01, January 2015 Issn (Online): 2348–7550.
Swathi. C., Anoop B. K D Anto Sahaya Dhas S. Perumal Sanker, “Comparison Of Different Image Preprocessing Methods Used For Retinal Fundus Images”, Proc. Ieee Conference On Emerging Devices And Smart Systems (Icedss 2017), 3-4 March 2017, Mahendra Engineering College, Tamilnadu, India.
Geetha Ramani, Sugirtharani S., Lakshmi B. “Automatic Detection of Glaucoma in Retinal Fundus Images through Image Processing and Data Mining Techniques”, International Journal of Computer Applications (0975 – 8887), Vol. 166, (No.8, May 2017).
Sundari. B, Sivaguru. S. “Early Detection of Glaucoma from Fundus Images by Using MATLAB GUI for Diabetic Retinopathy”, International Journal of Innovative Research in Computer and Communication Engineering, An ISO 3297: 2007 Certified Organization, Vol. 5, Issue 1, (January 2017).
Thomas K¨ohler, Joachim Hornegger, MarkusMayer, Georg Michelson. “Quality-Guided Image Denoising for Low-Cost Fundus Imaging”, Thomas.Koehler@informatik.uni-erlangen.de.
Claro M., Leonardo Santos, Wallinson Silva, Fliavio Araiujo, Nayara MouraAutomatic. “Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction”, CLEI ELECTRONIC JOURNAL, VOL. 19, (AUGUST 2016), NUM. 2, P. 4.
Fumero, Francisco, et Al. "Rim-One: An Open Retinal Image Database for Optic Nerve Evaluation." Computer-Based Medical Systems (Cbms), 2011 24th International Symposium On. Ieee, 2011.
Median Filter, Https://En.Wikipedia.Org/Wiki/Median_Filter
Mean Filter, Https://Www. Markschulze.Net/Java/Meanmed. Html
Wang, Zhou, and Alan C. Bovik. "Mean Squared Error: Love It Or Leave It? A New Look At Signal Fidelity Measures." Ieee Signal Processing Magazine 26.1 (2009): 98-117.
Bahaghighat, Mahdi, and Seyed Ahmad Motamedi. "Psnr Enhancement in Image Streaming Over Cognitive Radio Sensor Networks." Etri Journal 39.5 (2017): 683-694.
Wang, Zhou, Alan C. Bovik, and Eero P. Simoncelli. "Structural Approaches to Image Quality Assessment." Handbook of Image and Video Processing 7 (2005): 18.