The DOI matches its Crossref record; 121 of the 121 references it cites were found in a registry or an index.
A review on medical image denoising algorithms
Abstract
Abstract Over the past two decades, medical imaging and diagnostic techniques have gained immense attraction due to the rapid development in computing, internet, data storage and wireless technology. The reflection of these advancements has become evident in the field of medicine and medical sciences which enables the diagnosis and treatment of various diseases in a more fruitful manner. Furthermore, medical imaging is frequently justified in the follow up of a disease which is already diagnosed and treated. Medical images like any other form of imaging techniques are susceptible to noise and artifacts. Noise can be random or white noise with an even frequency distribution or frequency dependent noise introduced by a device's mechanism or signal processing algorithms. The presence of noise makes the images unclear and may perplex the identification and analysis of diseases which may result heavy losses including deaths. Hence, denoising of medical images is a mandatory and essential pre-processing technique for further medical image processing stages. The aim of this paper is to conduct a detailed analysis of the different denoising techniques used for medical imaging modalities which include the 2D/3D Ultrasound (US), Magnetic Resonance (MR), Computed Tomography (CT) and Positron Emission Tomography (PET) images.
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Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Scale-space and edge detection using anisotropic diffusion
Perona, P., & Malik, J. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.56205 · Cited by 12,119
Image denoising by sparse 3-D transform-domain collaborative filtering
Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.901238 · Cited by 9,283
Ideal spatial adaptation by wavelet shrinkage
Donoho, D. L., & Johnstone, I. M. (1994) · Biometrika · doi:10.1093/biomet/81.3.425 · Cited by 8,760
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177
A Non-Local Algorithm for Image Denoising
Buades, A., Coll, B., & Morel, J.-M. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.38 · Cited by 7,616
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Elad, M., & Aharon, M. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881969 · Cited by 5,321
FSIM: A Feature Similarity Index for Image Quality Assessment
Zhang, L., Zhang, L., Mou, X., & Zhang, D. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2109730 · Cited by 5,203
The contourlet transform: an efficient directional multiresolution image representation
Do, M. N., & Vetterli, M. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.859376 · Cited by 3,952
Digital Image Enhancement and Noise Filtering by Use of Local Statistics
Lee, J.-S. (1980) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1980.4766994 · Cited by 2,728
Metadata
- Authors
- Mohd Sagheer, S. V., & George, S. N.
- Venue
- Biomedical Signal Processing and Control
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 61
- Pages
- 102036
- ISSN
- 1746-8094 (ISSN-L)
- OpenAlex
- W3037582100
- Cited by
- 316
- References
- 121
- FWCI
- 13.34
- Type
- journal article
- Language
- en
- Open access
- closed
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