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A review on self-adaptation approaches and techniques in medical image denoising algorithms

  • 1218: Engineering Tools and Applications in Medical Imaging
  • Published:
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Abstract

Noise is a definite degeneration of medical images that interferes with the diagnostic process in clinical medicine. Although many denoising algorithms have been developed to improve the visual quality of medical images, failure to noise adaptation has been identified as a critical limitation of many existing denoising algorithms. Therefore, the objective of this study is to conduct an in-depth review to investigate and classify the various self-adaptive approaches and techniques implemented in recent medical image denoising applications. The articles published from the year 2015 have been retrieved from the web of science core collection database focusing on four medical imaging modalities, such as radiography, magnetic resonance imaging, computed tomography, and ultrasound. The analysis of the applications has emphasized the unique algorithmic components used to achieve the self-adaptability in detailed. Moreover, the strengths and weaknesses of those applications have been reviewed according to the various adaptive denoising approaches. Finally, this review highlights the limitations of existing adaptive denoising algorithms and open research directions for further studies of the domain.

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Abbreviations

ML:

Machine Learning

CT:

Computed Tomography

MRI:

Magnetic Resonance Imaging

US:

Ultrasonography

ANLM:

Adaptive Non-local Means

NLM:

Non-local Means

BM3D:

Block Matching Three Dimention

LDCT:

Low-dose CT

LRA:

Low Rank Approximation

SVD:

Singular Value Decomposition

TV:

Total Variation

ADF:

Anisotropic Diffusion Filtering

AMM:

Adaptive Mathematical Morphology

PCA:

Principle Component Analysis

LMMSE:

Linear Minimum Mean Square Error

MLE:

Maximum Likelihood Estimation

EM:

Expectation Maximization

MRF:

Markov Random Field

MAP:

Maximum a Posterior

DWT:

Discrete Wavelet Transform

SURE:

Stein’s Unbiased Risk Estimator

ANN:

Artificial Neural Network

FLANN:

Function Link Artificial Neural Network

CNN:

Convolutional Neural Networks

ResNet:

Residual CNN

GAN:

Generative Adversarial Networks

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Acknowledgements

This work was supported by the Fundamental Research Grant Scheme [FRGS/1/2019/TK04/UM/01/2], Ministry of Higher Education, Malaysia, the research grant of University of Malaya [IIRG012C-2019], and the World Bank-funded Accelerating Higher Education Expansion and Development Operation, Sri Lanka [Grant number: AHEAD/PhD/R1-PART-2/ENG&TECH/105].

Funding

This work was supported by the Fundamental Research Grant Scheme [FRGS/1/2019/TK04/UM/01/2], Ministry of Higher Education, Malaysia, the research grant of University of Malaya [IIRG012C-2019], and the World Bank-funded Accelerating Higher Education Expansion and Development Operation, Sri Lanka [Grant number: AHEAD/PhD/R1-PART-2/ENG&TECH/105].

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  1. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia

    K. A. Saneera Hemantha Kulathilake & Nor Aniza Abdullah

  2. Department of Computing, Faculty of Applied Sciences, Rajarata University of Sri Lanka, Mihintale, Sri Lanka

    K. A. Saneera Hemantha Kulathilake

  3. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia

    Aznul Qalid Md Sabri

  4. Department of Computer Science, Faculty of Applied Sciences, University of Sri Jayewardenepura, Nugegoda, Sri Lanka

    A. M. R. Ravimal Bandara

  5. Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, 50603, Kuala Lumpur, Malaysia

    Khin Wee Lai

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Kulathilake, K.A.S.H., Abdullah, N.A., Sabri, A.Q.M. et al. A review on self-adaptation approaches and techniques in medical image denoising algorithms. Multimed Tools Appl 81, 37591–37626 (2022). https://doi.org/10.1007/s11042-022-13511-w

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