Evaluation of Spatial Filtering Techniques in Retinal Fundus Images
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
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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.
Fundus Images, Spatial Filtering, MSE, PSNR, SSIM
To cite this article
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. doi: 10.11648/j.ajai.20180202.11
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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.
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