Novel Bayesian multiscale method for speckle removal in medical ultrasound images

By Alin M. Achim, Anastasios Bezerianos, Panagiotis Tsakalides. IEEE Transactions on Medical Imaging, 2001

648 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/8bd6f6d3ee5b897e0944f4367d96ea7052851c49

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Automated breast cancer detection and classification using ultrasound images: A survey (2015, background): “Most filters are traditional techniques in spatial domain and can be categorized as linear and nonlinear filters.”
Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion (2002, background): “It should be noted that some authors suggested the use of a generalization of the Gaussian distribution, namely, the alpha-stable distribution, to model such noise and handle the data with a degree of nonlinearity that corresponds to the deviation from the Gaussian model [27].”
Despeckling of medical ultrasound images (2006, methodology): “Below, a brief overview of this method is provided so as to render the presentation self-contained.”
Oriented Speckle Reducing Anisotropic Diffusion (2007, methodology): “…distribution , applying a preprocessing procedure to decorrelate the data as proposed in [19], comparing the performance of our technique with wavelet-based denoising proposed in [46]–[48], and evaluating the performance of the filter as a preprocessing tool for automatic segmentation algorithms.”
A versatile wavelet domain noise filtration technique for medical imaging (2003, background): “ficient [3] to model the realistic images well.”
Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, neutral): “All these methods require considerable A u t h o r ' s p e r s o n a l”
SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling (2003, background): “It is recognized that parametric Bayesian processing presupposes proper modeling for the prior probability density function (PDF) of the signal [18].”
Bibliography on stable distributions, processes and related topics (2004, neutral): “(1999), Achim et al. (2002), Achim et al.”
Ultrasound image segmentation: a survey (2006, methodology): “There is also an extensive literature on speckle reduction which has been proposed as a pre-segmentation step; recent works include wavelets based methods [199]–[202], anisotropic diffusion methods [60], [113], [203]–[205] and others [86], [180], [206]–[210].”
Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery (2005, methodology): “It has been shown that the logarithmic compression affects the speckle noise statistics in such a way that the local mean becomes proportional to the local variance rather than the standard deviation [24], [26], [28], [31] [see also (8)].”