Bio-Medical Image Denoising using Wavelet Transform
L. M. Satapathy1, P. Das2, A. Shatapathy3, A. K. Patel4
1L. M. Satapathy, Department of Electrical and Electronics Engineering, Siksha ‘O’ Anusandhan, Deemed to be University, Bhubaneswar, India.
2P. Das, Department of EE, Indira Gandhi Institute of Technology, Sarang, Odisha, India.
3A Shatapathy, A K Patel, Department of Electrical and Electronics Engineering, Siksha ‘O’ Anusandhan, Deemed to be University, Bhubaneswar, India.
Manuscript received on 10 April 2019 | Revised Manuscript received on 18 May 2019 | Manuscript published on 30 May 2019 | PP: 2479-2484 | Volume-8 Issue-1, May 2019 | Retrieval Number: A9125058119/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Now a day’s images are used in various medical science applications. A good quality image is highly required for proper diagnosis. This paper proposes a novel hybridization technique is proposed to improve the image quality of degraded image by the noise. The decomposition based proposed method initially separates the image into four parts as LL, LH, HL and HH. The high frequency information of the decomposed image is de-noised using the conventional denoising techniques. The resultant image is reconstructed by applying the inverse wavelet transform. In this process the coefficients of the wavelet preserves the useful information corresponding to the image structure, while suppresses the noisy elements. Experiments were conducted on various medical images available in public domain to compare the performance of the proposed algorithm with respect to the conventional methods such as Wiener filter, Median filter and wavelet soft threshold. The validity of the presented approach is subjectively quantified in terms of PSNR,MSE and structural similarity. The experimental results demonstrate that the proposed algorithm outperforms the existing denoising methods. For medical images, the PSNR and the SSIM values improve by using the proposed technique over various denoising approaches.
Index Terms: DWT, IDWT, Image de Noising, Medical Images, Ssim.
Scope of the Article: Biomedical Computing