A versatile wavelet domain noise filtration technique for medical imaging

By Aleksandra Pizurica, Wilfried Philips, Ignace L. Lemahieu, Marc Acheroy. IEEE Transactions on Medical Imaging, 2003

588 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9f2f648e544a3f60b00bc68dfcbdcf08eb851d63

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In Computer Vision and Pattern Recognition

Citations in the corpus

A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images (2013, background): “An interesting example of despeckling not pertaining SAR but ultrasound images and based on statistical classification of signal/noise wavelet coefficients is presented in [88].”
Noise and Signal Estimation in Magnitude MRI and Rician Distributed Images: A LMMSE Approach (2008, methodology): “AND EDGE PRESERVATION Fig. 16. Original MRI image from [ 14 ].”
MRI denoising using Non-Local Means (2008, methodology): “algorithms, the ADF (Perona and Malik, 1990) and a modern wavelet-based denoising algorithm (Pizurica et al., 2003).”
A survey on the magnetic resonance image denoising methods (2014, methodology): “Tan and Shi [56] compared the performance of multiwavelets based denoising of MR images with AMPT [53] and Pizurica’s versatile wavelet noise filtration techniques [52] by means of RMSE, SNR and CNR.”
Automated breast cancer detection and classification using ultrasound images: A survey (2015, background): “Applications of hard and soft thresholding can be found in [61,62,73–75].”
Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review (2015, methodology): “in [104] used the filtering technique proposed by [105] to denoise T2-W-MRI which was based on joint detection and estimation theory [106].”
Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. (2013, background): “This article reviews some of the most recent advances in breast cancer detection/diagnosis using CAD systems developed for mammography and ultrasound.”
Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal Means (2010, methodology): “Not surprisingly, several wavelet-based denoising methods have been proposed for MR images [10], [11], [13], [27], [ 28 ].”
Mathematics of Signal Processing (2004, background): “Applications of wavelets to medical imaging are found in [187], as well as in the other papers in that special issue.”
Noise Reduction for Magnetic Resonance Images via Adaptive Multiscale Products Thresholding (2003, methodology): “In the noise reduction technique of Pizurica [16], the interscale correlation information is exploited to classify the wavelet coefficients.”