Noise reduction of low-dose computed tomography using the multi-resolution total variation minimization algorithm

Shih, Chuen-Ming; Chang, Shou-Yi; Liu, Yan Lin; Wu, Jen-Ming

doi:10.1117/12.2007543

Cited by 5 publications

(2 citation statements)

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“…In this study, we used a Gaussian median filter to remove the random noise. The denoising procedures for LDCT, including filtering algorithms and reconstruction techniques, have been proposed (33). Filtration can be performed in the spatial domain or in the transformed domain.…”

Section: Discussionmentioning

confidence: 99%

“…Filtration can be performed in the spatial domain or in the transformed domain. In the spatial domain, the Gaussian is one of the filters which are most frequently used based in the local region (33). With the algorithm, the boundary between the fatty tissue and the fibroglandular tissue became visually distinguishable, which could facilitate the segmentation of the fibroglandular tissue.…”

Section: Discussionmentioning

confidence: 99%

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Opportunistic Breast Density Assessment in Women Receiving Low-dose Chest Computed Tomography Screening

Chen

Chan

et al. 2016

Academic Radiology

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Rationale and Objectives Low dose chest computed tomography (LDCT), increasingly being used for screening of lung cancer, may also be used to measure breast density, which is proven as a risk factor for breast cancer. In this study we developed a segmentation method to measure quantitative breast density on CT images and correlated with MR density. Materials and Methods Forty healthy females receiving both LDCT and breast magnetic resonance imaging (MRI) were studied. A semi-automatic method was applied to quantify the breast density on LDCT images. The intra-, inter-operator reproducibility was evaluated. The volumetric density on MRI was obtained by using a well-established automatic template-based segmentation method. The breast volume (BV), fibroglandular tissue volume (FV), and percent breast density (PD) measured on LDCT and MRI were compared. Results The measurements of BV, FV, and PD on LDCT images yields highly consistent results, with the intraclass correlation coefficient (ICC) of 0.999 for BV, 0.977 for FV, and 0.966 for PD for intra-operator reproducibility; and ICC of 0.953 for BV, 0.974 for FV, and 0.973 for PD for inter-operator reproducibility. The BV, FV, and PD measured on LDCT and MRI were well correlated (all r≥0.90). Bland-Altman plots showed that a larger BV and FV were measured on LDCT compared with MRI. Conclusions The preliminary results showed that quantitative breast density can be measured from LDCT, and that our segmentation method could yield a high reproducibility on the measured volume and percent breast density. The results measured on LDCT and MRI were highly correlated. Our results showed that LDCT may provide valuable information about breast density for evaluating breast cancer risk.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Opportunistic Breast Density Assessment in Women Receiving Low-dose Chest Computed Tomography Screening

Chen

Chan

et al. 2016

Academic Radiology

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Internet of Medical Things: A CT Image Denoising in Tetrolet Domain

Diwakar

Pandey

Sharma

et al. 2021

2021 5th International Conference on Information Systems and Computer Networks (ISCON)

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CT image noise reduction based on adaptive wiener filtering with Wavelet packet thresholding

Diwakar

Kumar

2014

2014 International Conference on Parallel, Distributed and Grid Computing

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Computed Tomography (CT) is streamlined in radiological diagnostics and has become an imperative tool in medical examinations. The difficulty that arises with the demand is to improve CT image quality without increasing dose. In this paper, Wavelet based noise reduction technique is proposed to improve image quality where adaptive Wiener filtering and Wavelet Packet Threshold (WPT) algorithm are applied. The Noisy CT image is decomposed using DWT, where approximation part is filtered using WPT algorithm and detail part is filtered by the adaptive Wiener ltering. By using the level dependent, the wavelet packet tree coe cients are calculated using optimal linear interpolation shrinkage function. Denoised image is acquired using wavelet packet reconstruction and inverse DWT. The value of the peak signal to noise ratio (PSNR) is used as the measure of image visual quality. Experimental results demonstrate that the proposed method improves the image visual quality in respect of noise removal and edge preservation.

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Noise reduction of low-dose computed tomography using the multi-resolution total variation minimization algorithm

Cited by 5 publications

References 0 publications

Opportunistic Breast Density Assessment in Women Receiving Low-dose Chest Computed Tomography Screening

Opportunistic Breast Density Assessment in Women Receiving Low-dose Chest Computed Tomography Screening

Internet of Medical Things: A CT Image Denoising in Tetrolet Domain

CT image noise reduction based on adaptive wiener filtering with Wavelet packet thresholding

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