Improving low-dose abdominal CT images by Weighted Intensity Averaging over Large-scale Neighborhoods

Yang, Chen; Chen, Wufan; Xia, Yin; Ye, Xianghua; Bao, Xudong; Luo, Limin; Feng, Qianjing; li, Yinsheng; Xiao-e, YU

doi:10.1016/j.ejrad.2010.07.003

Cited by 84 publications

(62 citation statements)

References 14 publications

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“…That study also suggested interesting variations of the objective function in (13), but the experimental evaluations are limited. Two interesting variations of dictionary-based iterative CT reconstruction are the tensor-based algorithm [81] and the method based on sparsifying transforms [70].…”

Section: Iterative Reconstruction Methodsmentioning

confidence: 95%

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Patch-based models and algorithms for image processing: a review of the basic principles and methods, and their application in computed tomography

Karimi¹,

Ward²

2016

Int J CARS

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Section: Iterative Reconstruction Methodsmentioning

confidence: 95%

“…Moreover, with a small search window, the patch size also has to be small to ensure effective denoising around the edges. Another study found that with a basic NLM denoising, the tube current setting can be reduced to 1/5 of that in routine abdominal imaging without jeopardizing the image quality [13].…”

Section: Post-processing Methodsmentioning

confidence: 99%

Patch-based models and algorithms for image processing: a review of the basic principles and methods, and their application in computed tomography

Karimi¹,

Ward²

2016

Int J CARS

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“…We will also try to apply the FSVM method to pathological brain detection [53][54][55], fruit classification [56], neuroscience [57,58], and CT Image Processing [59][60][61]. In addition, its stability and convergence [62,63] will be analyzed.…”

Section: Resultsmentioning

confidence: 99%

Identification of Green, Oolong and Black Teas in China via Wavelet Packet Entropy and Fuzzy Support Vector Machine

Wang

Yang

Zhang

et al. 2015

Entropy

103

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Abstract:To develop an automatic tea-category identification system with a high recall rate, we proposed a computer-vision and machine-learning based system, which did not require expensive signal acquiring devices and time-consuming procedures. We captured 300 tea images using a 3-CCD digital camera, and then extracted 64 color histogram features and 16 wavelet packet entropy (WPE) features to obtain color information and texture information, respectively. Principal component analysis was used to reduce features, which were fed into a fuzzy support vector machine (FSVM). Winner-take-all (WTA) was introduced to help the classifier deal with this 3-class problem. The 10 × 10-fold stratified cross-validation results show that the proposed FSVM + WTA method yields an overall recall rate of 97.77%, higher than 5 existing methods. In addition, the number of reduced features is only five, less than or equal to existing methods. The proposed method is effective for tea identification.

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“…Therefore, lots of studies have proven that the type of noise affecting CT images is the additive white Gaussian noise (Gravel et al 2004;Attivissimo et al 2010;Sun et al 2013). Numerous issues contributed to the existence of noise with CT images such as image acquisition modes, transmission, storage and display devices (Trinh et al 2012), errors in photon counting statistics (Chen et al 2011;Rahim et al 2012), imaging hardware problems, finite exposure time (Pham 2012), missing data through the image acquisition procedure or even problems in sensors and detectors of the CT imaging system, absorption of fewer amounts of x-ray photons (Chen et al 2011) and limitations of the exposure to radiation on the patient (Zhu et al 2012). Currently, certain types of CT imaging systems possess the capability to achieve isotropic acquisition of the whole chest with submillimetre resolution within a single breath hold.…”

Section: Introductionmentioning

confidence: 99%

Phase-preserving approach in denoising computed tomography medical images

Al-Ameen

Sulong

Rehman

et al. 2014

Computer Methods in Biomechanics and Biomedical Engineering: Im

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The denoising procedure attenuates the image noise while preserving its edges and fine details. In computed tomography (CT), images are degraded by additive white Gaussian noise because of different acquisition and system errors. Due to noise existence, specialists may encounter certain difficulties to analyse or extract the useful information from noisy images. This article presents a novel implementation of the phase-preserving algorithm to denoise CT images. The phase preserving is a powerful noise reduction algorithm, but it tends to remove specific details from the processed images supposing them as noise. Therefore, a Wiener filter that uses 2D Gaussian point spread function is used along with a modified version of the latter algorithm to reduce the noise and conserve the minor medical details. The performance of the proposed approach is assessed on naturally and synthetically degraded CT images using the universal image quality indexand peak signal-to-noise ratio accuracy metrics. Results show major improvement not only in noise attenuation but also in preserving the small details.

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Improving low-dose abdominal CT images by Weighted Intensity Averaging over Large-scale Neighborhoods

Cited by 84 publications

References 14 publications

Patch-based models and algorithms for image processing: a review of the basic principles and methods, and their application in computed tomography

Patch-based models and algorithms for image processing: a review of the basic principles and methods, and their application in computed tomography

Identification of Green, Oolong and Black Teas in China via Wavelet Packet Entropy and Fuzzy Support Vector Machine

Phase-preserving approach in denoising computed tomography medical images

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