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Enhanced Empirical Wavelet Transform for Denoising of Fundus Images

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Soft Computing Systems (ICSCS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 837))

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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.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore, India

    C. Amala Nair & R. Lavanya

Authors
  1. C. Amala Nair
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  2. R. Lavanya
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Corresponding author

Correspondence to R. Lavanya .

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Editors and Affiliations

  1. Department of Computer Science, Faculty of Electrical Engineering and Computer Science VÅ B-TUO, Ostrava-Poruba, Czech Republic

    Ivan Zelinka

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Roman Senkerik

  3. School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, Odisha, India

    Ganapati Panda

  4. Baselios Mathews II College of Engineering, Kerala, India

    Padma Suresh Lekshmi Kanthan

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Amala Nair, C., Lavanya, R. (2018). Enhanced Empirical Wavelet Transform for Denoising of Fundus Images. In: Zelinka, I., Senkerik, R., Panda, G., Lekshmi Kanthan, P. (eds) Soft Computing Systems. ICSCS 2018. Communications in Computer and Information Science, vol 837. Springer, Singapore. https://doi.org/10.1007/978-981-13-1936-5_13

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  • DOI: https://doi.org/10.1007/978-981-13-1936-5_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1935-8

  • Online ISBN: 978-981-13-1936-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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