3-D Gabor-based anisotropic diffusion for speckle noise suppression in dynamic ultrasound images

Chen, Haobo; Xu, Haohao; Shi, Peng; Gong, Yuchen; Qiu, Zhen; Shi, Lei; Zhang, Qi

doi:10.1007/s13246-020-00969-x

Cited by 8 publications

(2 citation statements)

References 37 publications

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“…Liu et al [23] proposed automatic segmentation algorithm using attentional convolutional network. Chen et al [24] constructed 3D filter to suppress ultrasonic image noise. Song et al [25] proposed deep networks for heart segmentation and explained the significance and challenges of heart segmentation.…”

Section: Introductionmentioning

confidence: 99%

A Heart Segmentation Algorithm Based on Dynamic Ultrasound

Tian

Zheng

2022

BioMed Research International

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The heart is one of the most important organs of the human body. The role of the heart is to promote blood flow and provide sufficient blood flow to organs and tissues. The research on the heart has important theoretical and clinical significance. Because of the noninvasive and intuitive display of ultrasound image, it can dynamically obtain the heart state and has become the main means to detect the heart dynamics. We analyze the characteristics of cardiac ultrasound image from the medical point of view and signal processing. The heart movement is periodic and rhythmic. The image signal can be decomposed. Firstly, the image is decomposed into high- and low-frequency signals to highlight different dimensional information. Then, the attention model was introduced, focusing on the heart region. Finally, the multidimensional network carrying model was established to achieve cardiac segmentation. The experimental results show that the AOM of the algorithm proposed in this paper reaches 92%, which has a certain degree of advancement and can assist doctors to make accurate diagnosis.

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Section: Introductionmentioning

confidence: 99%

A Heart Segmentation Algorithm Based on Dynamic Ultrasound

Tian

Zheng

2022

BioMed Research International

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“…While other filters like Lee [9] or Kuan [10], Frost [11], and Wiener filter [12] are also very effective for denoising. Table 1 contains previous work done [7], [13], [22], [23], [14]- [21].…”

Section: Introductionmentioning

confidence: 99%

A Log-Exponential Shrinkage Technique for De-Noising of Ultrasound Images for Breast Cancer

Singh

Saini

Sood

2021

Preprint

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Ultrasound in diagnostic imaging is well known for its safety and accessibility. But its efficiency for diagnosis is always limited by the presence of noise. So, in this study, a Log-Exponential shrinkage technique is presented for denoising of ultrasound images. A Combinational filter was designed for the removal of additive noise without losing any details. The speckle noise after homomorphic transformation follows Gaussian distribution and the conventional median estimator has very low accuracy for Gaussian distribution. The scale parameter calculated from the sub-band coefficients after homomorphic transformation was utilized to design the estimator. For shrinkage of wavelet coefficients, a multi-scale thresholding function was designed, with better flexibility. The proposed technique was tested for both medical and standard images. A significant improvement was observed in the estimation of speckle noise variance. For quantitative evaluation of the proposed technique with existing denoising methods, Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Peak Signal to Noise Ratio (PSNR) were used. At the highest noise variance, the minimum improvement achieved by the proposed denoising technique in PSNR, SSIM, and MSE was 10.65%, 23.21%, and 30.46% respectively.

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