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A comprehensive review of denoising techniques for abdominal CT images

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Abstract

Computed Tomography (CT) is one of the effective imaging modality in medical sciences that assist in diagnosing various pathologies inside the human body. Despite considerable advancement in acquisition speed, signal to noise ratio and image resolution of computed tomography imaging technology, CT images are still affected by noise and artifacts. A tradeoff between the amount of noise reduced and conservation of genuine image details has to be made in such a way that it enhances the clinically relevant image content. Therefore, noise reduction in medical images is an important and challenging task, as it helps to improve the performance of other image processing procedures such as segmentation or classification to perform better diagnosis by clinicians. Different techniques have been suggested in the literature on denoising of CT images, and each technique has its own presumptions, benefits, and drawbacks. To the best of our knowledge, no survey paper was found in the literature that compares the performance of various denoising techniques for CT images. This study aims to compare the capabilities of several notable and contemporary denoising techniques in the presence of different types of noise present in abdominal CT images. This comparative analysis helps to determine the most suitable denoising technique for practitioners and researchers that can be used in real life scenarios. Furthermore, the advantages and disadvantages of considered denoising methods have also been discussed along with some recommendations for further research in this area.

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Acknowledgements

This research work has been financially supported by University Grant Commission (UGC), New Delhi, India. Additionally, the authors would like to thank PGIMER Chandigarh for providing the image data set for carrying out this research.

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Correspondence to Ravinder Kaur.

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Kaur, R., Juneja, M. & Mandal, A.K. A comprehensive review of denoising techniques for abdominal CT images. Multimed Tools Appl 77, 22735–22770 (2018). https://doi.org/10.1007/s11042-017-5500-5

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