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.

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).

scholarly journals Despeckling Techniques for Ultrasound Images

2020 ◽  
Vol 9 (4) ◽  
pp. 769-772
Keyword(s):  
Imaging Modality ◽  
Image Interpretation ◽  
Speckle Noise ◽  
Disease Diagnosis ◽  
Ultrasound Images ◽  
Medical Procedure ◽  
Reduction Techniques ◽  
Diagnosis Of Cancer ◽  
Harmful Radiation

Biomedical imaging shows a substantial role in the era for diagnosis of cancer. Ultrasound (US) imaging modality is widely used in comparison to other modalities for disease diagnosis because it not being or involve in invasive medical procedure. US is a real-time imaging modality, economically, reliable and not uses harmful radiation during the diagnosis. The noise present in US, reduces the visuality and quality of US images is called speckle noise. It degrades the fine details of US image which causes difficulty in effective feature calculation, analysis and edge detection. Speckle noise effects image interpretation and causes misdiagnosis due to bed quality of the images. Many speckle noise reduction techniques have been investigated by various researchers for noise free images. In this paper, a detailed overview about different despeckling filters is presented with different evaluation parameters. This study will help the researchers to select efficient despeckling technique for preprocessing of US images.

Denoising Ultrasound Medical Images

2015 ◽  
Vol 5 (1) ◽  
pp. 1-14
Author(s):  
A.S.C.S.Sastry ◽  
P.V.V.Kishore MIEE ◽  
Ch.Raghava Prasad ◽  
M.V.D.Prasad
Keyword(s):  
Image Quality ◽  
Medical Images ◽  
Ultrasound Image ◽  
Similarity Index ◽  
Speckle Noise ◽  
Inverse Transformation ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Soft Thresholding

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).

The Development of Wavelet Transform and its Application in Image Denoise

2013 ◽  
Vol 694-697 ◽  
pp. 2003-2008
Author(s):  
Ming Hong Dai
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Filter Bank ◽  
Experimental Studies ◽  
Visual Quality ◽  
Simulation Experiments ◽  
Design And Implementation ◽  
Direction Filter ◽  
Related Design ◽  
Better Than

The paper introduces Laplace pyramid, Ridgelet and Curvelet principle, structure and methods, and their denoising experimental studies. It also introduces the traditional direction filter of principle, structure and methodology, and the simulation experiments show that its image denoising PSNR is slightly lower than wavelet but denoising image visual quality is better than former. To that end, proposed a new direction filters that uniform direction filter banks and non-uniform direction filters, proved filter passband condition and related design and implementation issues were discussed. nonlinear experiment shows that the new direction filter bank was better than the wavelet.

DAUBECHIES COMPLEX WAVELET TRANSFORM BASED TECHNIQUE FOR DENOISING OF MEDICAL IMAGES

2007 ◽  
Vol 07 (04) ◽  
pp. 663-687 ◽  
Author(s):  
ASHISH KHARE ◽  
UMA SHANKER TIWARY
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Medical Image ◽  
Medical Images ◽  
Universal Method ◽  
Complex Wavelet Transform ◽  
Shrinkage Function ◽  
Daubechies Complex Wavelet Transform ◽  
Complex Wavelet

Wavelet based denoising is an effective way to improve the quality of images. Various methods have been proposed for denoising using real-valued wavelet transform. Complex valued wavelets exist but are rarely used. The complex wavelet transform provides phase information and it is shift invariant in nature. In medical image denoising, both removal of phase incoherency as well as maintaining the phase coherency are needed. This paper is an attempt to explore and apply the complex Daubechies wavelet transform for medical image denoising. We have proposed a method to compute a complex threshold, which does not depend on any assumed model of noise. In this sense this is a "universal" method. The proposed complex-domain shrinkage function depends on mean, variance and median of wavelet coefficients. To test the effectiveness of the proposed method, we have computed the input and output SNR and PSNR of various types of medical images. The method gives an improvement for Gaussian additive, Speckle and Salt-&-Pepper noise as well as for the mixture of these noise types for a range of noisy images with 15 db to 30 db noise levels and outperforms other real-valued wavelet transform based methods. The application of the proposed method to Ultrasound, X-ray and MRI images is demonstrated in the experiments.

scholarly journals Denoising of Medical Ultrasound Images using Wavelet Transform with Bilateral Filter

2019 ◽  
Vol 8 (4) ◽  
pp. 8113-8116
Keyword(s):  
Wavelet Transform ◽  
Bilateral Filter ◽  
Intrinsic Noise ◽  
Speckle Noise ◽  
Computer Assisted ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Edge Preservation ◽  
Neighborhood Relationship ◽  
Speckle Filtering

Medical image degradation contains a significant impact on image quality and therefore affects the human interpretation and also the accuracy of computer assisted diagnostics techniques, unfortunately ultrasound images are principally degraded by an intrinsic noise known as speckle noise. Therefore, de- speckle filtering may be pre-processing step in medical ultrasound images. During this paper we propose a new image de-noising technique is the combination of bilateral filter and wavelet transform. The main contribution of this paper is within the use of a new neighborhood relationship to develop a new multi-scale bilateral filter. Experimental outcomes validate the usefulness and also the correctness of the proposed filter in edge preservation and speckle noise reduction for medical ultrasound images.

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.

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