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.

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).

Speckle Noise Diffusion in Knee Articular Cartilage Ultrasound Images

2020 ◽  
Vol 16 (6) ◽  
pp. 739-751
Author(s):  
Muhammad Ali Shoaib ◽  
Md Belayet Hossain ◽  
Yan Chai Hum ◽  
Joon Huang Chuah ◽  
Maheza Irna Mohd Salim ◽  
...  
Keyword(s):  
Articular Cartilage ◽  
Contrast Ratio ◽  
Speckle Noise ◽  
Absolute Error ◽  
Cartilage Injury ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Low Contrast ◽  
Reduction Techniques ◽  
Articular Cartilage Injury

Background: Ultrasound (US) imaging can be a convenient and reliable substitute for magnetic resonance imaging in the investigation or screening of articular cartilage injury. However, US images suffer from two main impediments, i.e., low contrast ratio and presence of speckle noise. Aims: A variation of anisotropic diffusion is proposed that can reduce speckle noise without compromising the image quality of the edges and other important details. Methods: For this technique, four gradient thresholds were adopted instead of one. A new diffusivity function that preserves the edge of the resultant image is also proposed. To automatically terminate the iterative procedures, the Mean Absolute Error as its stopping criterion was implemented. Results: Numerical results obtained by simulations unanimously indicate that the proposed method outperforms conventional speckle reduction techniques. Nevertheless, this preliminary study has been conducted based on a small number of asymptomatic subjects. Conclusion: Future work must investigate the feasibility of this method in a large cohort and its clinical validity through testing subjects with a symptomatic cartilage injury.

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 Perbandingan Teknik Reduksi Derau Speckle Pada Citra Ultrasonograpi Payudara

2018 ◽  
Vol 4 (2) ◽  
pp. 27-36
Author(s):  
Yuli Triyani
Keyword(s):  
Breast Cancer ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Reduction Techniques ◽  
Noise Filter ◽  
Incidence And Mortality ◽  
Structural Content ◽  
Aided Diagnosis

Breast cancer is the most commonly diagnosed cancer with the highest prevalence, incidence, and mortality rate for females in Indonesia and worldwide. Ultrasonography is a recommended modality for breast cancer, because it is comfortable, radiation free and it can be widely used. However, ultrasound images often occur in quality degradation caused by speckle noise that appears during image acquisition. It causes difficulty for radiologists or Computer Aided Diagnosis (CAD) systems to diagnose these images. Some techniques are proposed for reducing the speckle noise. This journal aims to compare the performance of 14 noise reduction techniques in breast ultrasound images. Quantitative testing was carried out on 58 breast ultrasound images and 3 artificial breast ultrasound image. The quantitative parameters are used include texture analysis (Mean, Variant, skewness, kurtosis, contrast and entropy) and evaluation of image quality (MSE, RMSE, SNR, SSIM, Structural content and Maximum Difference). The qualitative testing was also carried out with the assessment of 3 radiology specialists on 3 samples of each reduction technique. Based on test results, the 3 best performance filters are DsFsrad, DsFamedian dan DsFhmedian. Keywords: Ultrasound, speckle noise, filter

scholarly journals Refinement for Ocular Ultrasound Images Quality by Utilizing Combination of Enhancement Techniques

2016 ◽  
Vol 9 (3) ◽  
pp. 37
Author(s):  
Zinah Rajab Hussein ◽  
Zaid Rajab Hussein
Keyword(s):  
Wavelet Coefficient ◽  
Low Cost ◽  
Ultrasound Image ◽  
Mapping Function ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Diagnostic Modality ◽  
Ocular Ultrasound ◽  
Wide Availability

Ultrasound has been used as a diagnostic modality for many intraocular diseases, due its safety, low cost, real time and wide availability. Unfortunately, ultrasound images suffer from speckle artifact that are tissue dependent. In this work, we will offer a method to reduce speckle noise and improve ultrasound image to raise the human diagnostic performance. This method combined undecimated wavelet transform with a wavelet coefficient mapping function: where UDWT used to eliminate the noise and a wavelet coefficient mapping function used to enhance the contrast of denoised images obtained from the first component. This methods can be used not only as a means for improving visual quality of medical images but also as a preprocessing module for computer-aided detection/diagnosis systems to improve the performance of screening and detecting regions of interest in images. The proposed method is experimentally evaluated via 60 ultrasound images of eye. It is demonstrated that the proposed method can further improve the image quality of ocular ultrasound; the results reveal the effectiveness and superiority of the proposed method.

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.

Interpretation and clinical use of chest radiographs

2021 ◽  
pp. 181-200
Author(s):  
Alexander Parkhomenko ◽  
Olga S Gurjeva ◽  
Tetyana Yalynska
Keyword(s):  
Imaging Modality ◽  
Image Interpretation ◽  
Central Venous Catheters ◽  
Chest Radiographs ◽  
Pacemaker Lead ◽  
Efficient Manner ◽  
New Information ◽  
Chest X Ray ◽  
Concise Information

Although chest X-ray (CXR) is a well-studied classic diagnostic tool, this easy-to-obtain, readily accessible imaging modality is still capable of giving physicians new information on patient conditions in an efficient manner. This chapter provides intensivists, cardiologists, cardiology fellows, and medical students with concise information on recent data on CXR imaging and interpretation and briefly reviews the most common medical conditions in which CXR is useful. It includes a brief review of a multistep approach to the evaluation of the quality of chest radiographs and the systematic description of imaged structures. This chapter covers water retention, air collection, and lung-related problems, gives information on monitoring of line and device placements (central venous catheters, tubes, pacemaker lead positions, etc.), and also focuses on the pitfalls of image interpretation in intensive care unit settings. In addition, the chapter reviews recent data on accuracy of other imaging modalities, in terms of describing certain abnormalities, as compared with regular chest radiographs.

scholarly journals FILTERING ALGORITHMS FOR DE-SPECKLE THE ULTRASOUND IMAGES OF BONE FRACTURE

2020 ◽  
Vol 4 (2) ◽  
pp. 40-42
Author(s):  
Muhammad Luqman Muhd Zain ◽  
Wan Faizura Wan Tarmizi ◽  
Ruzlaini Ghoni
Keyword(s):  
Bone Fracture ◽  
Signal To Noise Ratio ◽  
Speckle Noise ◽  
Measurement Tool ◽  
Ultrasound Images ◽  
Hybrid Technique ◽  
Fracture Detection ◽  
Signal To Noise ◽  
Filtering Algorithms

Ultrasound images are popularly known to contain speckle noise that degrades the quality of the images for good and fast interpretation in many areas of medicine, especially for bone fracture detection. This necessitates the need for robust de-speckling techniques for clinical practice. Therefore, a study was carried out to reduce speckle using filtering algorithms such as Wiener, Average, Median and Wavelets. This paper discusses the level of improvement obtained through these filtering algorithms using the peak signal to noise ratio (PSNR) as a measurement tool. The results of our work presented in this paper suggest that the combination of Daubechies–Wiener which we call as a hybrid technique, gave the best performance, which is a new contribution in this field. This de-speckling algorithm can be further developed and evaluated at a larger scale.

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).

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