A stochastic approach to ultrasound despeckling
ABSTRACT A novel stochastically driven filtering method to despeckle B mode ultrasound images is presented. This method is motivated by viewing the pixel values as a stochastic process and removing outliers, where outliers are defined by local extrema. These outliers are removed by local averaging. This produces another image with new outliers (local extrema) and the process is iteratively repeated. With each iteration homogeneous regions become smoother while edges that defined these regions remain preserved. To evaluate the performance of our proposed method in satisfying these two opposing goals we develop a modified Fisher discriminant contrast metric. Larger values of this metric indicate better performance in reducing each intraregion or intraclass variance and increasing the difference of interregion or interclass means
- SourceAvailable from: Peter Tay
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- "Since the publication of the Lee filter , numerous despeckling techniques –, ,  have been proposed that utilized the same basic adaptive filtering concept. The general concept of these filters is to adaptively determine some degree of local smoothing that should be performed. "
ABSTRACT: Images produced by ultrasound systems are adversely hampered by a stochastic process known as speckle. A despeckling method based upon removing outlier is proposed. The method is developed to contrast enhance B-mode ultrasound images. The contrast enhancement is with respect to decreasing pixel variations in homogeneous regions while maintaining or improving differences in mean values of distinct regions. A comparison of the proposed despeckling filter is compared with the other well known despeckling filters. The evaluations of despeckling performance are based upon improvements to contrast enhancement, structural similarity, and segmentation results on a Field II simulated image and actual B-mode cardiac ultrasound images captured in vivo.IEEE Transactions on Image Processing 03/2010; 19(7):1847-60. DOI:10.1109/TIP.2010.2044962 · 3.11 Impact Factor
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- "More recently,  proposes a novel stochastically driven filtering method with constant and variable size windows. A simpler version of this filter with constant window, is proposed in . In , a novel despeckling method iteratively removes outliers by determining the local mean and standard deviation from an adaptively varying window. "
ABSTRACT: Multiplicative noise is often present in medical and biological imaging, such as magnetic resonance imaging (MRI), Ultrasound, positron emission tomography (PET), single photon emission computed tomography (SPECT), and fluorescence microscopy. Noise reduction in medical images is a difficult task in which linear filtering algorithms usually fail. Bayesian algorithms have been used with success but they are time consuming and computationally demanding. In addition, the increasing importance of the 3-D and 4-D medical image analysis in medical diagnosis procedures increases the amount of data that must be efficiently processed. This paper presents a Bayesian denoising algorithm which copes with additive white Gaussian and multiplicative noise described by Poisson and Rayleigh distributions. The algorithm is based on the maximum a posteriori (MAP) criterion, and edge preserving priors which avoid the distortion of relevant anatomical details. The main contribution of the paper is the unification of a set of Bayesian denoising algorithms for additive and multiplicative noise using a well-known mathematical framework, the Sylvester-Lyapunov equation, developed in the context of the Control theory.IEEE Transactions on Image Processing 10/2008; 17(9):1522-39. DOI:10.1109/TIP.2008.2001398 · 3.11 Impact Factor
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Conference Paper: Bayesian Non Local Means-Based Speckle filtering[Show abstract] [Hide abstract]
ABSTRACT: In ultrasound (US) imaging, denoising is intended to improve quantitative image analysis techniques. In this paper, a new version of the non local (nl) means filter adapted for US images is proposed. Originally developed for Gaussian noise removal, a Bayesian framework is used to adapt the NL means filter for speckle noise. Experiments were carried out on synthetic data sets with different speckle simulations. Results show that our NL means-based speckle filter outperforms the classical implementation of the NL means filter, as well as two other speckle adapted denoising methods (SRAD and SBF filters).Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on; 06/2008