A new algorithm for shot noise removal in medical ultrasound images based on alpha-stable model

Zha, Daifeng; Qiu, Tianshuang

doi:10.1002/acs.893

Cited by 12 publications

(9 citation statements)

References 15 publications

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“…We used the Matlab Image Processing Toolbox implementation [37] of the median filter called median2 . Like the median filter, the Wiener filter is a popular baseline comparison method for shot and speckle noise removal both in ultrasound and OCT images [7,21,25]. The Wiener filter is a linear filter mostly suited for images degraded by additive noise.…”

Section: Image Denoising Algorithmsmentioning

confidence: 99%

“…Due to its deleterious effects on coherent imaging systems such as ultrasound and OCT, there has been significant effort to characterize and reduce noise [2-21]. The two most common noise sources are shot noise, which is additive in nature and can be adequately described by the Additive White Gaussian Noise (AWGN) process, and speckle noise, which is multiplicative in nature and harder to eliminate due to its signal dependency.…”

Section: Introductionmentioning

confidence: 99%

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Denoising and 4D visualization of OCT images

Gargesha¹,

Jenkins²,

Rollins³

et al. 2008

Opt. Express

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We are using Optical Coherence Tomography (OCT) to image structure and function of the developing embryonic heart in avian models. Fast OCT imaging produces very large 3D (2D + time) and 4D (3D volumes + time) data sets, which greatly challenge ones ability to visualize results. Noise in OCT images poses additional challenges. We created an algorithm with a quick, data set specific optimization for reduction of both shot and speckle noise and applied it to 3D visualization and image segmentation in OCT. When compared to baseline algorithms (median, Wiener, orthogonal wavelet, basic non-orthogonal wavelet), a panel of experts judged the new algorithm to give much improved volume renderings concerning both noise and 3D visualization. Specifically, the algorithm provided a better visualization of the myocardial and endocardial surfaces, and the interaction of the embryonic heart tube with surrounding tissue. Quantitative evaluation using an image quality figure of merit also indicated superiority of the new algorithm. Noise reduction aided semi-automatic 2D image segmentation, as quantitatively evaluated using a contour distance measure with respect to an expert segmented contour. In conclusion, the noise reduction algorithm should be quite useful for visualization and quantitative measurements (e.g., heart volume, stroke volume, contraction velocity, etc.) in OCT embryo images. With its semi-automatic, data set specific optimization, we believe that the algorithm can be applied to OCT images from other applications.

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Section: Image Denoising Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Denoising and 4D visualization of OCT images

Gargesha¹,

Jenkins²,

Rollins³

et al. 2008

Opt. Express

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“…Single scale representation is difficult to discriminate signal from noise 2. Sensitive to the size and shape of the filter window Wavelet approaches [60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79] Transform image to wavelet domain and remove noise by modifying wavelet coefficients 1. In wavelet domain, the statistics of the signals are simplified DWT and IDWT computations increase time complexity 2.…”

Section: Methods Description Advantage Disadvantagementioning

confidence: 99%

“…Bayesian rules [60,[63][64][65][66][67][68][69][70][71]. It relies on the knowledge of the wavelet coefficient statistics.…”

Section: Wavelet Despeckling Under Bayesian Framework An Alternate Amentioning

confidence: 99%

Automated breast cancer detection and classification using ultrasound images: A survey

Cheng

Shan

et al. 2010

Pattern Recognition

633

346

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Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In order to eliminate the operator dependency and improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is a valuable and beneficial means for breast cancer detection and classification. Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification. In this paper, the approaches used in these stages are summarized and their advantages and disadvantages are discussed. The performance evaluation of CAD system is investigated as well.

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“…Despite the fact that the software experimentation of image de-noising [77][78][79][80][81][82][83] has gained much effort from the research community, it is worth noting that very few effort in hardware implementation.…”

Section: Hardware Implementation Of Image De-noisingmentioning

confidence: 99%

Efficient reconfigurable architectures for 3D medical image compression

Ahmad

Amira

2009

2009 International Conference on Field-Programmable Technology

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A AbstractRecently, the more widespread use of three-dimensional (3-D) imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US) have generated a massive amount of volumetric data. These have provided an impetus to the development of other applications, in particular telemedicine and teleradiology. In these fields, medical image compression is important since both efficient storage and transmission of data In summary, this research is tackling the issues of massive 3-D medical volumes data that requires compression as well as hardware implementation to accelerate the slowest operations in the system. Results obtained also reveal a significant achievement in terms of the architecture efficiency and applications performance.

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A new algorithm for shot noise removal in medical ultrasound images based on alpha-stable model

Cited by 12 publications

References 15 publications

Denoising and 4D visualization of OCT images

Denoising and 4D visualization of OCT images

Automated breast cancer detection and classification using ultrasound images: A survey

Efficient reconfigurable architectures for 3D medical image compression

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