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

Speckle noise removal in medical ultrasound images is a challenging task. In this paper, a new model is proposed to removal speckle noise, alternating direction method of multipliers algorithm is employed to solve the new energy minimization model. The convexity, existence, and uniqueness of the new energy minimization model’s solution are proved. Series of experiments are designed in this paper. Numerical results show that the new algorithm can reduce the step effect effectively obtain good results in visual effect and quantitative measures by comparing with some traditional models.

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Novel Ultrasound Images Shot Noise Removal Algorithm Based On 2-D Wavelet Decomposition and Stable Distribution Model

2007 1st International Conference on Bioinformatics and Biomedical Engineering ◽

10.1109/icbbe.2007.220 ◽

2007 ◽

Author(s):

Daifeng Zha

Keyword(s):

Shot Noise ◽

Wavelet Decomposition ◽

Stable Distribution ◽

Noise Removal ◽

Distribution Model ◽

Ultrasound Images

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Improved Adaptive Wavelet Thresholding for Effective Speckle Noise Reduction in Low Contrast Medical Images

Journal of Circuits System and Computers ◽

10.1142/s0218126619501767 ◽

2019 ◽

Vol 28 (10) ◽

pp. 1950176 ◽

Cited By ~ 2

Author(s):

P. Sreelatha ◽

M. Ezhilarasi

Keyword(s):

Contrast Enhancement ◽

Speckle Noise ◽

Noise Removal ◽

Ultrasound Images ◽

Medical Ultrasound ◽

Gamma Correction ◽

Significant Information ◽

Adaptive Wavelet ◽

Low Contrast ◽

Image Contrast Enhancement

Informative images endure from poor contrast and noise during image acquisition. Significant information retrieval necessitates image contrast enhancement and removal of noise as a prerequisite before any further processing can be done. Dominant applications with low contrast images affected by speckle noise are medical ultrasound images. The objective of this work is to improve the effectiveness of the preprocessing stage in medical ultrasound images by enhancing the image while retaining its structural characteristics. For image enhancement, this work proposes to develop an automatic contrast enhancement technique using cumulative histogram equalization and gamma correction based on the image. For noise removal, this work proposes an algorithm Gamma Correction with Exponentially Adaptive Threshold (GCEAT) which suggests the use of GC for contrast enhancement along with a new wavelet-based adaptive soft thresholding technique for noise removal. The proposed GCEAT-based image de-noising is validated with other enhancement and noise removal techniques. Experimental results with low contrast synthetic and actual ultrasound images show that the suggested proposed system performs better than existing contrast enhancement techniques. Encouraging results were obtained with medical ultrasound images in terms of Peak-Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index Measure (SSIM) and Average Intensity (AI).

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Guassian and Speckle Noise Removal from Ultrasound Images using Bivariate Shrinkage by Dual Tree Complex Wavelet Transform

i-manager’s Journal on Image Processing ◽

10.26634/jip.2.2.3400 ◽

2015 ◽

Vol 2 (2) ◽

pp. 1-5

Author(s):

Devanand Bhonsle ◽

◽

Vivek Chandra ◽

G.R. Sinha ◽

◽

...

Keyword(s):

Wavelet Transform ◽

Speckle Noise ◽

Noise Removal ◽

Ultrasound Images ◽

Complex Wavelet Transform ◽

Complex Wavelet ◽

Bivariate Shrinkage

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Motion blur invariant for estimating motion parameters of medical ultrasound images

Scientific Reports ◽

10.1038/s41598-021-93636-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Barmak Honarvar Shakibaei Asli ◽

Yifan Zhao ◽

John Ahmet Erkoyuncu

Keyword(s):

Image Quality Assessment ◽

Medical Image Analysis ◽

Main Idea ◽

Delta Function ◽

Motion Blur ◽

Ultrasound Images ◽

Medical Ultrasound ◽

Fetal Ultrasound ◽

Dirac Delta ◽

Motion Parameters

AbstractHigh-quality medical ultrasound imaging is definitely concerning motion blur, while medical image analysis requires motionless and accurate data acquired by sonographers. The main idea of this paper is to establish some motion blur invariant in both frequency and moment domain to estimate the motion parameters of ultrasound images. We propose a discrete model of point spread function of motion blur convolution based on the Dirac delta function to simplify the analysis of motion invariant in frequency and moment domain. This model paves the way for estimating the motion angle and length in terms of the proposed invariant features. In this research, the performance of the proposed schemes is compared with other state-of-the-art existing methods of image deblurring. The experimental study performs using fetal phantom images and clinical fetal ultrasound images as well as breast scans. Moreover, to validate the accuracy of the proposed experimental framework, we apply two image quality assessment methods as no-reference and full-reference to show the robustness of the proposed algorithms compared to the well-known approaches.

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