CT image denoising using NLM and correlation‐based wavelet packet thresholding. Issue 5 (1st May 2018)

• Record Type: Journal Article
• Authors: Diwakar, Manoj; Kumar, Manoj
• Is Part Of: IET image processing. Volume 12:Issue 5(2018)
• Page Start: 708
• Page End: 715
• Publication Date: 2018-05-01
• Subjects: image denoising -- medical image processing -- image filtering -- computerised tomography -- wavelet transforms -- image enhancement; CT image denoising -- correlation‐based wavelet packet thresholding -- radiation dose reduction -- computed tomography images -- low‐dose CT images -- noise reduction method -- signal‐to‐noise ratio -- uncorrelated noise -- nonlocal mean filter -- NLM filter -- wavelet packet thresholding -- noise suppression -- visual quality -- image quality index -- peak SNR -- entropy difference; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0639
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : The impact of radiation dose is directly related to the quality of computed tomography (CT) images. Low‐dose CT images are degraded with the noise and other factors. Noise reduction methods are very helpful to enhance the noisy CT images with a possibility to increase the signal‐to‐noise ratio (SNR) and have a scope for further reduction of radiation dose. In this study, a denoising scheme is proposed which is applicable only for two identical images with uncorrelated noise. In the proposed scheme, a non‐local means (NLM) filter is used to denoise the first input image and a wavelet packet thresholding to denoise the second input image. Results of NLM filter are analysed and found excellent for noise suppression but missing the small structures of the input image. To recover that, the proposed scheme is using correlation‐based wavelet packet thresholding. The final outcomes of the proposed scheme are excellent in terms of noise suppression and structure preservation. The proposed scheme is compared with existing methods and it is observed that performance of the proposed method is superior to existing methods in terms of visual quality, image quality index, peak SNR and entropy difference.