Despeckling method of ultrasound images using closed-form shrinkage function based on cauchy distribution in wavelet domain

2020 ◽  
Vol 18 (04) ◽  
pp. 2050026
Author(s):  
Kyong-Il Kim ◽  
Soon-Ic Bahng ◽  
Ryong-Nam Choe
Keyword(s):  
Closed Form ◽  
Image Denoising ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
The Other ◽  
Ultrasound Images ◽  
Wavelet Domain ◽  
Wavelet Coefficients ◽  
Shrinkage Function ◽  
Map Estimator

Speckle suppression and elimination are very important to improve the visual quality of ultrasound image and the diagnostic ability of the diseases. An effective technique of image denoising based on discrete wavelet transform is to employ a Bayesian maximum a posteriori (MAP) estimator. To suppress and remove the speckle noise using MAP estimator effectively, it must assign correctly the shrinkage function based on appropriate probability density functions (PDFs) for the wavelet coefficients of logarithmically transformed noise-free ultrasound image and speckle noise. In this paper, we introduce a new closed-form shrinkage function that is an analytical solution of a Bayesian MAP estimator for despeckling of the ultrasound images effectively in wavelet domain. We employ a Cauchy prior and Gaussian PDF to model the wavelet coefficients of logarithmically transformed noise-free ultrasound image and speckle noise, respectively. Firstly, we derive the CauchyShrinkGMAP that is a closed-form shrinkage function. In addition, we estimate the noise variance and parameter of MAP estimator. Next, we evaluate the despeckling performance of wavelet image denoising method using the CauchyShrinkGMAP compared to various despeckling method using median and Wiener filters, hard and soft thresholding and GaussShrinkGMAP and MCMAP3N shrinkage function. The experiment results show that PSNR of new closed-form shrinkage function is highest, MSE is smallest, and the correlation coefficient ([Formula: see text]) and SSIM are closer to one than the other existing image denoising methods for noisy synthetic ultrasound images at different speckle noise levels. Also, experiment results show that ENL of new closed-form shrinkage function is highest and that of EN and SD is smallest than the other existing image denoising methods for noisy real ultrasound image.

scholarly journals Research on ultrasound image of interventional catheterization processing method based on wavelet transform and fuzzy theory

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 5187-5194
Author(s):  
Chenyang Liang ◽  
Ning He
Keyword(s):  
Wavelet Transform ◽  
Fuzzy Theory ◽  
Ultrasound Image ◽  
Low Frequency ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Wavelet Coefficients ◽  
Image Processing Algorithm ◽  
Exponential Transformation ◽  
Interventional Catheterization

Interventional catheterization can help patients to accurately assess the condition, early diagnosis and intervention. Confirming the location of catheter by ultrasound has the advantages of real-time imaging, non-invasive, radiative, fast and convenient. Due to speckle noise and similar acoustic impedance, ultrasound images are not clear. In this paper an ultrasonic image processing algorithm based on wavelet transform and fuzzy theory is proposed. First, logarithmic transformation of ultrasound images is used to convert multiplicative noise into additive noise. Then the wavelet coefficients of the image are obtained by multiscale wavelet transform. The high frequency wavelet coefficients of the image are denoised by thresholding, and the low-frequency wavelet coefficients of the image are processed by fuzzy enhancement. Finally, the processed image is obtained through wavelet reconstruction and exponential transformation. Experiments show that this proposed method can effectively improve the visual effect of images.

scholarly journals Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhuxiang Shen ◽  
Wei Li ◽  
Hui Han
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Similarity Index ◽  
Speckle Noise ◽  
Disease Diagnosis ◽  
Function Optimization ◽  
Threshold Function ◽  
Ultrasound Images ◽  
Better Than

To explore the utilization of the convolutional neural network (CNN) and wavelet transform in ultrasonic image denoising and the influence of the optimized wavelet threshold function (WTF) algorithm on image denoising, in this exploration, first, the imaging principle of ultrasound images is studied. Due to the limitation of the principle of ultrasound imaging, the inherent speckle noise will seriously affect the quality of ultrasound images. The denoising principle of the WTF based on the wavelet transform is analyzed. Based on the traditional threshold function algorithm, the optimized WTF algorithm is proposed and applied to the simulation experiment of ultrasound images. By comparing quantitatively and qualitatively with the traditional threshold function algorithm, the advantages of the optimized WTF algorithm are analyzed. The results suggest that the image is denoised by the optimized WTF. The mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM) of the images are 20.796 dB, 34.294 dB, and 0.672 dB, respectively. The denoising effect is better than the traditional threshold function. It can denoise the image to the maximum extent without losing the image information. In addition, in this exploration, the optimized function is applied to the actual medical image processing, and the ultrasound images of arteries and kidneys are denoised separately. It is found that the quality of the denoised image is better than that of the original image, and the extraction of effective information is more accurate. In summary, the optimized WTF algorithm can not only remove a lot of noise but also obtain better visual effect. It has important value in assisting doctors in disease diagnosis, so it can be widely applied in clinics.

scholarly journals Texture Based Quality Analysis of Simulated Synthetic Ultrasound Images Using Local Binary Patterns

2017 ◽  
Author(s):  
Prerna Singh ◽  
Ramakrishnan Mukundan ◽  
Rex De Ryke
Keyword(s):  
Quality Assessment ◽  
Image Quality Assessment ◽  
Ultrasound Image ◽  
Texture Feature ◽  
Ground Truth ◽  
Speckle Noise ◽  
Local Binary Patterns ◽  
Quality Analysis ◽  
Ultrasound Images ◽  
Ground Truth Data

Speckle noise reduction is an important area of research in the field of ultrasound image processing. Several algorithms for speckle noise characterization and analysis have been recently proposed in the area. Synthetic ultrasound images can play a key role in noise evaluation methods as they can be used to generate a variety of speckle noise models under different interpolation and sampling schemes, and can also provide valuable ground truth data for estimating the accuracy of the chosen methods. However, not much work has been done in the area of modelling synthetic ultrasound images, and in simulating speckle noise generation to get images that are as close as possible to real ultrasound images. An important aspect of simulated synthetic ultrasound images is the requirement for extensive quality assessment for ensuring that they have the texture characteristics and gray-tone features of real images. This paper presents texture feature analysis of synthetic ultrasound images using local binary patterns (LBP) and demonstrates the usefulness of a set of LBP features for image quality assessment. Experimental results presented in the paper clearly show how these features could provide an accurate quality metric that correlates very well with subjective evaluations performed by clinical experts.

Denoising Ultrasound Medical Images

2017 ◽  
pp. 761-775
Author(s):  
A.S.C.S. Sastry ◽  
P.V.V. Kishore ◽  
Ch. Raghava Prasad ◽  
M.V.D. Prasad
Keyword(s):  
Image Quality ◽  
Medical Images ◽  
Ultrasound Image ◽  
Similarity Index ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Soft Thresholding ◽  
Block Based

Medical ultrasound imaging has revolutioned the diagnostics of human body in the last few decades. The major drawback of ultrasound medical images is speckle noise. Speckle noise in ultrasound images is because of multiple reflections of ultrasound waves from hard tissues. Speckle noise degrades the medical ultrasound images lessening the visible quality of the image. The aim of this paper is to improve the image quality of ultrasound medical images by applying block based hard and soft thresholding on wavelet coefficients. Medical ultrasound image transformation to wavelet domain uses debauchee's mother wavelet. Divide the approximate and detailed coefficients into uniform blocks of size 8×8, 16×16, 32×32 and 64×64. Hard and soft thresholding on these blocks of approximate and detailed coefficients reduces speckle noise. Inverse transformation to original spatial domain produces a noise reduced ultrasound image. Experiments on medical ultrasound images obtained from diagnostic centers in Vijayawada, India show good improvements to ultrasound images visually. Quality of improved images in measured using peak signal to noise ratio (PSNR), image quality index (IQI), structural similarity index (SSIM).

An Adaptive Rayleigh-Laplacian Based MAP Estimation Technique for Despeckling SAR Images using Stationary Wavelet Transform

2013 ◽  
Vol 4 (4) ◽  
pp. 88-102
Author(s):  
Amlan Jyoti Das ◽  
Anjan Kumar Talukdar ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Parameter Estimation ◽  
Wavelet Transform ◽  
Speckle Noise ◽  
Posteriori Probability ◽  
Wavelet Coefficients ◽  
Stationary Wavelet Transform ◽  
Sar Images ◽  
Edge Information ◽  
A Posteriori Probability ◽  
Map Estimator

Removal of speckle noise from Synthetic Aperture Radar (SAR) images is an important step before performing any image processing operations on these images. This paper presents a novel Stationary Wavelet Transform (SWT) based technique for the purpose of removing the speckle noise from the SAR returns. Maximum a posteriori probability (MAP) condition which uses a prior knowledge is used to estimate the noise free wavelet coefficients. The proposed MAP estimator is designed for this purpose which uses Rayleigh distribution for modeling the speckle noise and Laplacian distribution for modeling the statistics of the noise free wavelet coefficients. The parameters required for MAP estimator is determined by technique used for parameter estimation after SWT. Moreover an Laplacian – Gaussian based MAP estimator is also applied and the parameter estimation is done using the same method used for the proposed algorithm. For the purpose of enhancing the visual quality and to restore more edge information, a wavelet based resolution enhancement technique is also used after applying the Inverse stationary Wavelet Transform (ISWT), using interpolation technique. The experimental results show that the proposed despeckling algorithm efficiently removes speckle noise from the SAR images and restores the edge information as well.

scholarly journals Using the method of renormalization with limitation for medical ultrasound image processing.

2020 ◽  
Vol 2020 (10) ◽  
Author(s):  
A.V. Kokoshkin ◽  
Keyword(s):  
Image Processing ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Bilateral Filtering ◽  
Ultrasound Image Processing ◽  
Images Processing

This article proposes the application of the technique of combining the modernized method of renormalization with limitation with subsequent bilateral filtering to improve the quality of medical ultrasound images. Processing according to this algorithm increases the overall contrast of the image, smoothes out speckle noise and makes it possible to cope well with determining the localization of significant objects. The presented results indicate a significant improvement in the quality of ultrasound images, which can serve as an auxiliary tool for medical workers when clarifying the diagnosis.

scholarly journals A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

2020 ◽  
Vol 1 (3) ◽  
pp. 78-91
Author(s):  
Muhammad Muhammad ◽  
Diyar Zeebaree ◽  
Adnan Mohsin Abdulazeez Brifcani ◽  
Jwan Saeed ◽  
Dilovan Asaad Zebari
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Region Of Interest ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Clustering Methods ◽  
Advantages And Disadvantages ◽  
Ultrasound Image Segmentation

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

scholarly journals A Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images: A Review

2020 ◽  
Vol 1 (3) ◽  
pp. 78-91
Author(s):  
Muhammad Muhammad ◽  
Diyar Zeebaree ◽  
Adnan Mohsin Abdulazeez Brifcani ◽  
Jwan Saeed ◽  
Dilovan Asaad Zebari
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Region Of Interest ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Clustering Methods ◽  
Advantages And Disadvantages ◽  
Ultrasound Image Segmentation

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

scholarly journals Novel image enhancement approaches for despeckling in ultrasound images for fibroid detection in human uterus

2021 ◽  
Vol 11 (1) ◽  
pp. 399-410
Author(s):  
Kaitheri Thacharedath Dilna ◽  
Duraisamy Jude Hemanth
Keyword(s):  
Image Processing ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Signal To Noise ◽  
Statistical Measures ◽  
Noise Ratio ◽  
Scanned Images

Abstract Ultrasonography is an extensively used medical imaging technique for multiple reasons. It works on the basic theory of echoes from the tissues under consideration. However, the occurrence of signal dependent noise such as speckle destroys utility of ultrasound images. Speckle noise is subject to the composition of image tissue and parameters of image. It reduces the effectiveness of many image processing steps and decreases human perception of fine details form ultrasound images. In many medical image processing methods, despeckling is used as the preprocessing step before segmentation and feature extraction. Many speckle reduction filters are proposed but while combining many techniques some speckle diagnostic information should be preserved. Removal of speckle noise from ultrasound image by preserving edges and added features is a great challenging task in ultrasound image restoration. This paper aims at a comprehensive description and comparison of reduction of speckle noise of ultrasound fibroid image. Many filters are applied on ultrasound scanned images and the performance is marked in terms of some statistical measures. Even though several despeckling filters are there for speckle reduction, all are not good for ultrasound scanned images. A comparison of quality measures such as mean square error, peak signal-to-noise ratio, and signal-to-noise ratio is done in ultrasound images in despeckling.

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