A Comparative Study in Wavelets, Curvelets and Contourlets as Denoising biomedical Images

Hamdi, Med Ali

doi:10.2478/v10248-012-0007-1

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…The performance of BEAM can be further improved by enforcing additional or different constraints. For example, further compression could be achieved through leveraging the smooth boundaries of the abdominal organs with alternative sparsifying transforms such as curvelets or contourlets . Furthermore, the mixture density could be modified to additionally enforce structure in the encoded phase (e.g., by constraining the phase accumulation from one offset to the next).…”

Section: Discussionmentioning

confidence: 99%

A bayesian method for accelerated magnetic resonance elastography of the liver

Ebersole

Ahmad

Rich

et al. 2018

Magnetic Resonance in Med

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By exploiting transform sparsity and magnitude consistency, accurate quantification of mean stiffness in the liver can be obtained at an acceleration rate of up to R = 6. This potentially enables the collection of three to four liver slices, as per clinical protocol, within a single breath-hold. Magn Reson Med 80:1178-1188, 2018. © 2018 International Society for Magnetic Resonance in Medicine.

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Section: Discussionmentioning

confidence: 99%

A bayesian method for accelerated magnetic resonance elastography of the liver

Ebersole

Ahmad

Rich

et al. 2018

Magnetic Resonance in Med

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“…In addition to linear methods such as Wiener filtering, nonlinear techniques including applying thresholding/shrinkage functions in transform domains have been reported in recent years. [3591819] In general, in image transform-based denoising approaches, two important issues should be considered. The first one is choosing proper transform and the other is selecting proper thresholding function.…”

Section: X-lets For Image Denoisingmentioning

confidence: 99%

The ellipselet transform

et al. 2019

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Background:A fair amount of important objects in natural images have circular and elliptical shapes. For example, the nucleus of most of the biological cells is circular, and a number of parasites such as Oxyuris have elliptical shapes in microscopic images. Hence, atomic representations by two-dimensional (2D) basis functions based on circle and ellipse can be useful for processing these images. The first researches have been done in this domain by introducing circlet transform.Methods:The main goal of this article is expanding the circlet to a new one with elliptical basis functions.Results:In this article, we first introduce a new transform called ellipselet and then compare it with other X-let transforms including 2D-discrete wavelet transform, dual-tree complex wavelet, curvelet, contourlet, steerable pyramid, and circlet transform in the application of image denoising.Conclusion:Experimental results show that for noises under 30, the ellipselet is better than other geometrical X-lets in terms of Peak Signal to Noise Ratio, especially for Lena which contains more circular structures. However, for Barbara which has fine structures in its texture, it has worse results than dual-tree complex wavelet and steerable pyramid.

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“…Denoising in Curvelet domain has better results for speckle noise reduction of ultrasonic scans' images, but; in some cases it cannot maintain all features of the scan's image. This is a well-known problem in the field of biomedical imaging and image processing [32]- [40].…”

Section: ------------------------------------------------------------mentioning

confidence: 99%

A Wavelet-Based Approach for Ultrasound Image Restoration

GadAllah¹,

Badawy²

2014

ijacsa

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Ultrasound's images are generally affected by speckle noise which is mainly due to the scattering phenomenon's coherent nature. Speckle filtration is accompanied with loss of diagnostic features. In this paper a modest new trial introduced to remove speckles while keeping the fine features of the tissue under diagnosis by enhancing image's edges; via Curvelet denoising and Wavelet based image fusion. Performance evaluation of our work is done by four quantitative measures: the peak signal to noise ratio (PSNR), the square root of the mean square of error (RMSE), a universal image quality index (Q), and the Pratt's figure of merit (FOM) as a quantitative measure for edge preservation. Plus Canny edge map which is extracted as a qualitative measure of edge preservation. The measurements of the proposed approach assured its qualitative and quantitative success into image denoising while maintaining edges as possible. A Gray phantom is designed to test our proposed enhancement method. The phantom results assure the success and applicability of this paper approach not only to this research works but also for gray scale diagnostic scans' images including ultrasound's Bscans.

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A Comparative Study in Wavelets, Curvelets and Contourlets as Denoising biomedical Images

Cited by 7 publications

References 16 publications

A bayesian method for accelerated magnetic resonance elastography of the liver

A bayesian method for accelerated magnetic resonance elastography of the liver

The ellipselet transform

A Wavelet-Based Approach for Ultrasound Image Restoration

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