Speckle noise removal applied to ultrasound image of carotid artery based on total least squares model

2016 ◽  
Vol 24 (5) ◽  
pp. 749-760
Author(s):  
Lei Yang ◽  
Jun Lu ◽  
Ming Dai ◽  
Li-Jie Ren ◽  
Wei-Zong Liu ◽  
...  
Keyword(s):  
Carotid Artery ◽  
Least Squares ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Noise Removal ◽  
Total Least Squares

scholarly journals Speckle Noise Removal by Energy Models with New Regularization Setting

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Bo Chen ◽  
Jinbin Zou ◽  
Weiqiang Zhang
Keyword(s):  
Total Variation ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Noise Removal ◽  
Energy Models ◽  
Energy Diffusion ◽  
Total Variation Model ◽  
Local Coordinates ◽  
Diffusion Speed ◽  
Adaptive Total Variation

In this paper, we introduce two novel total variation models to deal with speckle noise in ultrasound image in order to retain the fine details more effectively and to improve the speed of energy diffusion during the process. Firstly, two new convex functions are introduced as regularization term in the adaptive total variation model, and then, the diffusion performances of Hypersurface Total Variation (HYPTV) model and Logarithmic Total Variation (LOGTV) model are analyzed mathematically through the physical characteristics of local coordinates. We have shown that the larger positive parameter in the model is set, the greater energy diffusion speed appears to be, but it will cause the image to be too smooth that required adequate attention. Numerical experimental results show that our proposed LOGTV model for speckle noise removal is superior to traditional models, not only in visual effect but also in quantitative measures.

scholarly journals A Median Filter With Evaluating of Temporal Ultrasound Image for Impulse Noise Removal for Kidney Diagnosis

2020 ◽  
Vol 1 (2) ◽  
pp. 71-77
Author(s):  
Rasheed Ihsan ◽  
Saman Almufti ◽  
Ridwan Marqas
Keyword(s):  
Information Quality ◽  
Kidney Diseases ◽  
Median Filter ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Noise Removal ◽  
Ultrasound Images ◽  
Examination Methods ◽  
Detection And Diagnosis ◽  
Temporal Dimensions

Ultrasound imaging helps the doctor to view the tissues and organs in the body's abdominal area with no ionization risks compared to other internal organ examination methods dependent on radiation. It offers highly precise renal imaging of suspected acute kidney diseases. This paper proposes temporary filtering methods to improve ultrasound images from ultrasonic kidney video. The proposed filters focus on the detection and diagnosis of kidney disease by processing consecutive images of the acquired kidney video. Extending the spatial median image filters to temporal dimensions after the picture frames are manually clipped and aligned in MATLAB by image processing Toolbox to suppress speckle noise, and enhance a doctor's diagnostic information quality.

scholarly journals An Efficient Two step Algorithm for Despeckling the Ultrasound Image

2019 ◽  
Vol 8 (6) ◽  
pp. 1074-1078
Keyword(s):  
Median Filter ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Noise Removal ◽  
Ultrasound Images ◽  
Mean Filter ◽  
Image Area ◽  
Step Algorithm ◽  
Image Details ◽  
Average Filter

The speckle noise presence in ultrasound images is a critical concern in medical image processing. It degrades the important features captured in an image and decreases the physician’s capacity to understand the image accurately. In recent years, numerous techniques have been proposed to de-noise the ultrasound images. In this paper, a new speckle noise removal algorithm has been proposed for medical ultrasound images. Based on the concepts of fuzzy logic and Coefficient of variation, the proposed algorithm first classifies the image area into three different regions such as homogeneous, edge and detail region. Next, average filter, median filter and an adaptive mean filter are employed to partition the unwanted noise from the pixels of different regions. Filter selection depends on the features of a region. The proposed algorithm develops image quality by removing maximum unwanted noise while protecting the important image details

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