scholarly journals Effects of various De-Speckling Filters on Brachial Plexus Ultrasound Imaging

2019 ◽  
Vol 8 (2) ◽  
pp. 5058-5065
Keyword(s):  
Brachial Plexus ◽  
Ultrasound Imaging ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Important Process

Medical Ultrasound images are generally corrupted by Speckle noise. It deteriorates the quality of ultrasound imaging and video that makes it difficult to observe visually. Because of which resolution and contrast of the image is reduced. Despeckling of medical US images is an important process for diagnostic of disease. In this paper effect of various existing despeckling filter on ultrasound images has been studied. All the filters have been implemented in a framework and result are observed in the form of various parameters such as GAE, MSE, SNR, SRMSE, PSNR, UIQI, SSIM, AD, SC, MD. The results obtained have been used for statistically comparing the performance of the filters. It is also analyzed that which type of filters are more suited for particular type of images, noise and other conditions. This will also provide guidelines for the researchers for designing of new filters in future.

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 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.

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).

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 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.

High-definition ultrasound imaging defines the paraneural sheath and fascial compartments surrounding the cords of the brachial plexus at the costoclavicular space and lateral infraclavicular fossa

2021 ◽  
pp. rapm-2020-102304
Author(s):  
Pornpatra Areeruk ◽  
Manoj Kumar Karmakar ◽  
Miguel A Reina ◽  
Louis Y H Mok ◽  
Ranjith Kumar Sivakumar ◽  
...  
Keyword(s):  
Brachial Plexus ◽  
Ultrasound Imaging ◽  
Brachial Plexus Block ◽  
Ultrasound Images ◽  
High Definition ◽  
Link Type ◽  
Before And After ◽  
Registration Number ◽  
Local Anesthetic Injection ◽  
Plexus Block

Background and objectivesThe paraneural sheath is a multilayered network of collagen fibers that surround the brachial plexus. Currently, there are no sonographic data on the paraneural sheath of the brachial plexus, which this study aimed to evaluate.MethodsUltrasound imaging datasets of 100 patients who received a costoclavicular brachial plexus block, using high-definition ultrasound imaging, were retrospectively reviewed. Video files, representing sonograms before and after the local anesthetic injection, from the costoclavicular space and lateral infraclavicular fossa were collated and reviewed by three experienced anesthesiologists. Frequency (yes/no) of ultrasound visualization of the paraneural sheath, septum, and the anterior and posterior compartments was assessed. Representative sonograms from the costoclavicular space and lateral infraclavicular fossa were visually correlated with archived cadaver microanatomic sections from the same location.ResultsDatasets of the 98 patients who achieved surgical anesthesia were evaluated. The paraneural sheath, septum, and the anterior and posterior compartments were visualized in 17.3%, 7.1%, 5.1% and 5.1%, respectively, at the costoclavicular space before the brachial plexus block; this contrasts (p<0.001) with their visibility post-block (94.9%, 75.5%, 75.5% and 75.5%, respectively). At the lateral infraclavicular fossa, the corresponding visibility of these structures post-block were 67.7%, 81.5%, 81.5% and 81.5%, respectively. Ultrasound images of the paraneural sheath and septum correlated well with that in the cadaver microanatomic sections.ConclusionWe have demonstrated the paraneural sheath and fascial compartments surrounding the cords of the brachial plexus at the costoclavicular space and lateral infraclavicular fossa using high-definition ultrasound imaging.Trial registration numberClinicalTrials.gov Registry (NCT04370184), (https://www.clinicaltrials.gov/).

scholarly journals A Novel Speckle Noise Removal Algorithm Based on ADMM and Energy Minimization Method

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Bo Chen ◽  
Yan Lv ◽  
Jinbin Zou ◽  
Wensheng Chen ◽  
Binbin Pan
Keyword(s):  
Energy Minimization ◽  
Speckle Noise ◽  
Noise Removal ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Minimization Method ◽  
Alternating Direction ◽  
Step Effect ◽  
New Energy ◽  
Series Of Experiments

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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