Volume 17, Issue 1 (March 2021)                   IJEEE 2021, 17(1): 1485-1485 | Back to browse issues page

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Petrov M. Shearlet-Based Adaptive Noise Reduction in CT Images. IJEEE 2021; 17 (1) :1485-1485
URL: http://ijeee.iust.ac.ir/article-1-1485-en.html
Shearlet-Based Adaptive Noise Reduction in CT Images
M. Petrov
Abstract:   (2233 Views)
The noise in reconstructed slices of X-ray Computed Tomography (CT) is of unknown distribution, non-stationary, oriented and difficult to distinguish from main structural information. This requires the development of special post-processing methods based on the local statistical evaluation of the noise component. This paper presents an adaptive method of reducing noise in CT images employing the shearlet domain in order to obtain such an estimate. The algorithm for statistical noise assessment takes into account the distribution of signal energy in different scales and directions. The method efficiently uses the strong targeted sensitivity of shearlet systems in order to reflect more accurately the anisotropic information in the image. Because of the complex characteristics of the noise in these images, the threshold constant is determined by means of the relative entropy change criterion. The comparative analysis, which has been conducted, shows that the proposed method achieves higher values for the Peak Signal-to-Noise Ratio (PSNR), as well as lower values for the Mean Squared Error (MSE), in comparison with the other methods considered. For the MATLAB’S Shepp Logan Phantom test image, the numerical value of this superiority is on average more than 23% for the first quantitative measure, and 37% for the second. Its efficiency, which is greater than that of the wavelet-based method, is confirmed by the results obtained – the edges have been preserved during noise reduction in real CT images.
Keywords: Entropy of Shannon , Shearlet Decomposition , Statistical Noise Reduction , X-Ray Computed Tomography
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  • A noise reduction method in CT images has been proposed. Its algorithm performs a local noise assessment by means of separate reconstructions of two non-intersecting subsets of the set of projections of a given image.
  • Shearlet systems have been opted in order to obtain such statistical assessment because of their highly directional presentation together with optimal sparse approximations of anisotropic information and rapid numerical realization.
  • The change rate criterion for the Shannon entropy is used to determine the soft threshold mask for processing the high-frequency coefficients of the averaged image obtained from the two input reconstructions.
  • The comparison between the proposed method and the techniques using certain threshold constants shows the advantage of the former in terms of both quantitative measures and visualization quality.

Type of Study: Research Paper | Subject: Biomedical Signal & Image Processing
Received: 2019/04/29 | Revised: 2020/04/06 | Accepted: 2020/04/10
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.