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

• Main Authors: Kulathilake, K. A. Saneera Hemantha, Abdullah, Nor Aniza, Sabri, Aznul Qalid Md, Bandara, A. M. R. Ravimal, Lai, Khin Wee
• Format: Article
• Published: Springer 2022
• Subjects: QA Mathematics; QA75 Electronic computers. Computer science
• Online Access: http://eprints.um.edu.my/41201/
• Institution: Universiti Malaya

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.