Title: Quantitative evaluation of denoising techniques of lung computed tomography images: an experimental investigation

Authors: Bikesh Kumar Singh; Neeti Nair; Patle Ashwini Falgun; Pankaj Jain

Addresses: Department of Biomedical Engineering, National Institute of Technology, Raipur, 492010, CG, India ' Department of Biomedical Engineering, National Institute of Technology, Raipur, 492010, CG, India ' Department of Biomedical Engineering, National Institute of Technology, Raipur, 492010, CG, India ' Department of Biomedical Engineering, National Institute of Technology, Raipur, 492010, CG, India

Abstract: Appropriate selection of denoising method is critical component of lung computed tomography (CT)-based computer aided diagnosis (CAD) systems since noises and artefacts may deteriorate the image quality significantly thereby leading to incorrect diagnosis. This study presents a comparative investigation of various techniques used for denoising lung CT images. Current practices, evaluation measures, research gaps and future challenges in this area are also discussed. Experiments on 20 real-time lung CT images indicate that Gaussian filter with 3 × 3 window size outperformed others achieving high picture signal-to-noise ratio (PSNR), Pratt's figure of merit (PFOM), signal-to-noise ratio (SNR) and root mean square error (RMSE) of 45.476, 97.964, 32.811, 0.948 and 0.008, respectively. Further, this approach also demonstrates good edge retrieval efficiency. Future work is needed to evaluate various filters in clinical practice along with segmentation, feature extraction, and classification of lung nodules in CT images.

Keywords: image denoising; lung computed tomography; computer aided diagnosis; CAD; image smoothening; edge preservation; quantitative evaluation; image contrast; picture signal-to-noise ratio; PSNR; image quality; noise attenuation; time domain; frequency domain.

DOI: 10.1504/IJBET.2022.120868

International Journal of Biomedical Engineering and Technology, 2022 Vol.38 No.2, pp.151 - 178

Received: 09 Aug 2018
Accepted: 18 Dec 2018

Published online: 15 Feb 2022 *

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