A CT Image Denoise Method Using Curvelet Transform

Jun-min, Deng; Li, Haiyun; Wu, Hao

doi:10.1007/978-3-642-21762-3_89

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…The wavelet subband decomposition imposes a relationship of width = (length) 2 and ensures an-isotropic or edge-preserving properties and incorporates parabolic scaling. The Discrete curvelet transform is divided into three steps.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Wavelet-based transforms are used nowadays for obtaining good results in image denoising. 2 Compared with the Fourier Transform, the wavelet transform gives better output for nonstationary signals as it functions in both the time and frequency domain. However, the wavelet transforms deal only with linear directions, and sometimes it is beneficial to have better directional refinement.…”

Section: Introductionmentioning

confidence: 99%

“…Wavelet transform can preserve edges better as they downscale a given image to horizontal, vertical, and diagonal details. Wavelet‐based transforms are used nowadays for obtaining good results in image denoising 2 …”

Section: Introductionmentioning

confidence: 99%

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Retinal image enhancement using adaptive histogram equalization tuned with nonsimilar grouping curvelet

Bala

Priya

Maik

2020

Int J Imaging Syst Tech

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Fundus images are broadly used by medical ophthalmologists to detect and assess any customary abnormalities. Fundus imaging sensors capture the eye's rigid portion, which characteristically covers the core, tangential retina, optic disc, and macula. Existing state‐of‐the‐art fundus sensors have the drawback of producing low contrast and noisy information, which makes scientific and algorithmic evaluation very complicated. This article proposes an Adaptive Histogram Equalization—Tuned with Nonsimilar Grouping Curvelet (HET‐NOSCU), which works through a joint denoising enhancement approach. The proposed algorithm's main contribution includes (i) use of curvelet features to better preserve edges during denoising. (ii) Adaptive enhancement using the histogram to prevent halo ringing and specular artifacts, which yields superior results than the very recently established state‐of ‐the‐art methods, using similar performing parameters such as peak signal to noise ratio (PSNR), structural similarity index (SSIM), and correlation coefficient (CoC). We observe an improvement of 17.66%, 0.93%, and 0.24%, respectively, for the above parameters.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Retinal image enhancement using adaptive histogram equalization tuned with nonsimilar grouping curvelet

Bala

Priya

Maik

2020

Int J Imaging Syst Tech

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“…However, to our knowledge, only few works in literature considered the wavelet approach to denoise CT lung images [2] [3]. In [2], the authors propose a fusion algorithm based on wavelet transform and canny operator to detect image edges, which may reduce the noise and obtain the continuous and distinct edges, whereas in [3], the authors combine Curvelet transformation with Monte-Carlo algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…In [2], the authors propose a fusion algorithm based on wavelet transform and canny operator to detect image edges, which may reduce the noise and obtain the continuous and distinct edges, whereas in [3], the authors combine Curvelet transformation with Monte-Carlo algorithm. Firstly, CT image's Curvelet decomposition is processed, then, MonteCarlo algorithm is used to estimate high frequency coefficients.…”

Section: Introductionmentioning

confidence: 99%

Denoising CT Images using wavelet transform

Gabralla¹,

Mahersia²,

Zaroug³

2015

ijacsa

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Abstract-Image denoising is one of the most significant tasks especially in medical image processing, where the original images are of poor quality due the noises and artifacts introduces by the acquisition systems. In this paper, we propose a new image denoising scheme by modifying the wavelet coefficients using soft-thresholding method, we present a comparative study of different wavelet denoising techniques for CT images and we discuss the obtained results. The denoising process rejects noise by thresholding in the wavelet domain. The performance is evaluated using Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE). Finally, Gaussian filter provides better PSNR and lower MSE values. Hence, we conclude that this filter is an efficient one for preprocessing medical images.

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