Biomedical image denoising with wavelets and Bayesian geometrical constraints

Malfait, Maurits; Roose, Dirk

doi:10.1109/iembs.1994.415406

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…When applied to MR images, the method compared quite favorably with the optimal space-invariant solution (Wiener filter); in particular, it produced images with much sharper edges and did not induce ringing artifacts [113]. Malfait et al proposed a stochastic extension of this approach using Markov random field models [58].…”

Section: Biomedical Imagingmentioning

confidence: 99%

A review of wavelets in biomedical applications

1996

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In this paper, we present an overview of the various uses of the wavelet transform (WT) in medicine and biology. We start by describing the wavelet properties that are the most important f o r biomedical applications. In particular, we provide an interpretation of the continuous wavelet transfom (CWT) as a prewhitening multiscale matched filter. We also briefy indicate the analogy between the WT and some of the biological processing that occurs in the early components of the auditory and visual system. We then review the uses of the WT f o r the analysis of I-D physiological signals obtained by phonocardiography, electrocardiography (ECG), and electroencephalography (EEG), including evoked response potentials. Next, we provide a survey of recent wavelef developments in medical imaging. These include biomedical image processing algorithms (e.g., noise reduction, image enhancement, and detection of microcalciJcations in ntammograms), image reconstruction and acquisition schemes (tomography, and magnetic resonance imaging (MRI)), and multiresolution methods for the registration and statistical analysis of functional images of the brain (positron emission tomography (PET) and functional MRI ( f l R I ) ) . In each case, we provide the reader with some general background information and a brief explanation of how the methods work.

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Section: Biomedical Imagingmentioning

confidence: 99%

A review of wavelets in biomedical applications

1996

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Wavelet-based image denoising using a Markov random field a priori model

Malfait¹,

Roose

1997

IEEE Trans. on Image Process.

274

166

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This paper describes a new method for the suppression of noise in images via the wavelet transform. The method relies on two measures. The first is a classic measure of smoothness of the image and is based on an approximation of the local Holder exponent via the wavelet coefficients. The second, novel measure takes into account geometrical constraints, which are generally valid for natural images. The smoothness measure and the constraints are combined in a Bayesian probabilistic formulation, and are implemented as a Markov random field (MRF) image model. The manipulation of the wavelet coefficients is consequently based on the obtained probabilities. A comparison of quantitative and qualitative results for test images demonstrates the improved noise suppression performance with respect to previous wavelet-based image denoising methods.

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Biomedical image denoising with wavelets and Bayesian geometrical constraints

Cited by 2 publications

References 4 publications

A review of wavelets in biomedical applications

A review of wavelets in biomedical applications

Wavelet-based image denoising using a Markov random field a priori model

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