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Enhanced Empirical Wavelet Transform for Denoising of Fundus Images

Publication Type : Journal Article

Publisher : Springer Verlag

Source : Communications in Computer and Information Science, Springer Verlag, Volume 837, p.116-124 (2018)

Url : https://link.springer.com/chapter/10.1007/978-981-13-1936-5_13

ISBN : 9789811319358

Keywords : Automated methods, Cost effectiveness, Cost-effective means, De-noising, Eye protection, Fundus photographs, glaucoma, image analysis, Image compression, Image denoising, Image Enhancement, Ophthalmology, Peak signal to noise ratio, Peak Signal to Noise Ratio (PSNR), Signal to noise ratio, Soft computing, Vision impairments, Wavelet transforms

Campus : Coimbatore

School : School of Engineering

Department : Electronics and Communication

Year : 2018

Abstract : Glaucoma is an ophthalmic pathology caused by increased fluid pressure in the eye, which leads to vision impairment. The evaluation of the Optic Nerve Head (ONH) using fundus photographs is a common and cost effective means of diagnosing glaucoma. In addition to the existing clinical methods, automated method of diagnosis can be used to achieve better results. Recently, Empirical Wavelet Transform (EWT) has gained importance in image analysis. In this work, the effectiveness of EWT and its extension called Enhanced Empirical Wavelet Transform (EEWT) in denoising fundus images was analyzed. Around 30 images from High Resolution Fundus (HRF) image database were used for validation. It was observed that EEWT demonstrates good denoising performance when compared to EWT for different noise levels. The mean Peak Signal to Noise Ratio (PSNR) improvement achieved by EEWT was as high as 67% when compared to EWT.

Cite this Research Publication : A. C. Nair and Dr. Lavanya R., “Enhanced Empirical Wavelet Transform for Denoising of Fundus Images”, Communications in Computer and Information Science, vol. 837, pp. 116-124, 2018.

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