scholarly journals Edge Enhancing Accelerated Diffusion Model for Speckle Denoising in Medical Imagery

2019 ◽  
Vol 29 ◽  
pp. 01009
Author(s):  
Arundhati Bagchi Misra ◽  
Chartese Jones ◽  
Hyeona Lim
Keyword(s):  
Medical Images ◽  
Lagrange Equation ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Total Variation Minimization ◽  
Pde Model ◽  
Medical Imagery ◽  
Wide Range ◽  
Automated Processing ◽  
Processing Techniques

Speckle noise occurs in a wide range of medical images due to sampling and digital degradation. Removing speckle noise from medical images is the key for further automated processing techniques like segmentation, and can help the clinicians with better diagnosis and therapy. We consider partial differential equation (PDE)-based denoising model which is a modified Euler-Lagrange equation derived from the total variation minimization functional with additional speckle noise constraints. The new PDE model is designed and optimized to rectify speckle noise and enhance edges present in medical imagery. Wealso develop the efficicient and stable discretization techniques for the corresponding speckle denoising model. The method is tested for several types of images including ultrasound images, and it is compared favorably to the conventional denoising model.

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).

Framelet transform and fuzzy clustering-based intelligent technique for speckle noise removal in ultrasound images

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Praveen Kumar Lendale ◽  
N.M. Nandhitha
Keyword(s):  
Performance Indicators ◽  
Medical Images ◽  
Speckle Noise ◽  
Noise Removal ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Clustering Methods ◽  
Edge Preservation ◽  
Content Type ◽  
Intelligent Technique

PurposeSpeckle noise removal in ultrasound images is one of the important tasks in biomedical-imaging applications. Many filtering -based despeckling methods are discussed in many existing works. Two-dimensional (2-D) transforms are also used enormously for the reduction of speckle noise in ultrasound medical images. In recent years, many soft computing-based intelligent techniques have been applied to noise removal and segmentation techniques. However, there is a requirement to improve the accuracy of despeckling using hybrid approaches.Design/methodology/approachThe work focuses on double-bank anatomy with framelet transform combined with Gaussian filter (GF) and also consists of a fuzzy kind of clustering approach for despeckling ultrasound medical images. The presented transform efficiently rejects the speckle noise based on the gray scale relative thresholding where the directional filter group (DFB) preserves the edge information.FindingsThe proposed approach is evaluated by different performance indicators such as the mean square error (MSE), peak signal to noise ratio (PSNR) speckle suppression index (SSI), mean structural similarity and the edge preservation index (EPI) accordingly. It is found that the proposed methodology is superior in terms of all the above performance indicators.Originality/valueFuzzy kind clustering methods have been proved to be better than the conventional threshold methods for noise dismissal. The algorithm gives a reconcilable development as compared to other modern speckle reduction procedures, as it preserves the geometric features even after the noise dismissal.

scholarly journals Speckle and Rician Noise Removal from Medical Images and Ultrasound Images

2020 ◽  
Vol 8 (5) ◽  
pp. 1851-1854
Keyword(s):  
Noise Reduction ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Speckle Noise ◽  
Noise Removal ◽  
Difficult Problem ◽  
Ultrasound Images ◽  
Simple Method ◽  
Different Types

In medical images, medical images are corrupted by different types of noise. It is important to get a precise picture and accurately observe the correspondence. Removing noise from medical images has become a very difficult problem in the field of the medical image. The most well-known noise reduction method, which is usually based on the local statistics of medical images, is efficient because of the noise reduction of medical images. In paper, an efficient and simple method for noise reduction from medical images is presented. The paper proposes a filtering system to combine both the Median filter and Gaussian filter to remove the Speckle noise form Medical and Ultrasound images. The image quality is measured through statistical quantities: Peak signal to noise ratio (PSNR). Experimental results show that the proposed system removes Speckle noise from medical images.

scholarly journals Fractional Calculus and Fuzzy Rule Based Filter for Despeckling the Medical Images

2018 ◽  
Vol 7 (4.10) ◽  
pp. 685
Author(s):  
Nageswari P ◽  
Rajan S ◽  
Manivel K
Keyword(s):  
Fractional Order ◽  
Medical Image ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Medical Image Analysis ◽  
Ultrasound Image ◽  
Fuzzy Rule ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Medical Ultrasound

Medical ultrasound imaging plays an important role in diagnosis of various complicated disorders. But, these ultrasound images are intrinsically degraded with speckle noise which harshly affects the image visual qualities and essential particulars. Hence, denoising is an unavoidable process in medical image processing.  In this paper, a new despeckling technique is presented for denoising the medical ultrasound images by employing fuzzy technique on co-efficient of variation and fractional order integration filter. The proposed technique has two steps. During first step, the noisy image pixels are classified into three regions by using fuzzy technique on co-efficient of variation and consequently, the proposed technique adaptively employs appropriate filters on the grouped pixels to reduce noise in the ultrasound image. In the second step, to obtain an effective denoising image, the fractional order integration filter is applied on the resulting image of step 1. The performance of the proposed technique is tested on various medical images in terms of Peak signal to noise ratio and speckle suppression index quality measures. Experimental results reveal that the proposed despeckling technique can efficiently reduce the speckle noise, protect the edges and preserves any other important structural details of an image. It is suggested that the proposed technique is employed as a preprocessing tool for medical image analysis and diagnosis. 

scholarly journals Performance Analysis of Gaussian, Median, Mean and Weiner Filters on Biomedical Image De-Noising

2020 ◽  
Author(s):  
Subrato Bharati ◽  
Prajoy Podder
Keyword(s):  
Noise Reduction ◽  
Medical Image ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Image Data ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Biomedical Image ◽  
Uniform Noise ◽  
Diagnostic Values

Noise reduction in medical images is a perplexing undertaking for the researchers in digital image processing. Noise generates maximum critical disturbances as well as touches the medical images quality, ultrasound images in the field of biomedical imaging. The image is normally considered as gathering of data and existence of noises degradation the image quality. It ought to be vital to reestablish the original image noises for accomplishing maximum data from images. Medical images are debased through noise through its transmission and procurement. Image with noise reduce the image contrast and resolution, thereby decreasing the diagnostic values of the medical image. This paper mainly focuses on Gaussian noise, Pepper noise, Uniform noise, Salt and Speckle noise. Different filtering techniques can be adapted for noise declining to improve the visual quality as well as reorganization of images. Here four types of noises have been undertaken and applied on medical images. Besides numerous filtering methods like Gaussian, median, mean and Weiner applied for noise reduction as well as estimate the performance of filter through the parameters like mean square error (MSE), peak signal to noise ratio (PSNR), Average difference value (AD) and Maximum difference value (MD) to diminish the noises without corrupting the medical image data.

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).

Ochrona dziedzictwa kulturowego a działania na dużych szybkozmiennych zbiorach danych – perspektywa kryminologiczna

2019 ◽  
pp. 5-22
Author(s):  
Szymon Buczyński
Keyword(s):  
New Technology ◽  
Big Data Analytics ◽  
Technological Innovations ◽  
Cultural Organizations ◽  
Econometric Methods ◽  
The Arts ◽  
Huge Data ◽  
Wide Range ◽  
Share Data ◽  
Processing Techniques

Recent technological revolutions in data and communication systemsenable us to generate and share data much faster than ever before. Sophisticated data tools aim to improve knowledge and boost confdence. That technological tools will only get better and user-friendlier over the years, big datacan be considered an important tool for the arts and culture sector. Statistical analysis, econometric methods or data mining techniques could pave theway towards better understanding of the mechanisms occurring on the artmarket. Moreover crime reduction and prevention challenges in today’sworld are becoming increasingly complex and are in need of a new techniquethat can handle the vast amount of information that is being generated. Thisarticle provides an examination of a wide range of new technological innovations (IT) that have applications in the areas of culture preservation andheritage protection. The author provides a description of recent technological innovations, summarize the available research on the extent of adoptionon selected examples, and then review the available research on the eachform of new technology. Furthermore the aim of this paper is to explore anddiscuss how big data analytics affect innovation and value creation in cultural organizations and shape consumer behavior in cultural heritage, arts andcultural industries. This paper discusses also the likely impact of big dataanalytics on criminological research and theory. Digital criminology supports huge data base in opposition to conventional data processing techniques which are not only in suffcient but also out dated. This paper aims atclosing a gap in the academic literature showing the contribution of a bigdata approach in cultural economics, policy and management both froma theoretical and practice-based perspective. This work is also a startingpoint for further research.

scholarly journals Towards Reduced CNNs for De-Noising Phase Images Corrupted with Speckle Noise

Photonics ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 255
Author(s):  
Marie Tahon ◽  
Silvio Montresor ◽  
Pascal Picart
Keyword(s):  
Phase Error ◽  
Speckle Noise ◽  
Experimental Conditions ◽  
Benchmark Data ◽  
Wide Range ◽  
Phase Images ◽  
Different Depths ◽  
Phase Data ◽  
Do So

Digital holography is a very efficient technique for 3D imaging and the characterization of changes at the surfaces of objects. However, during the process of holographic interferometry, the reconstructed phase images suffer from speckle noise. In this paper, de-noising is addressed with phase images corrupted with speckle noise. To do so, DnCNN residual networks with different depths were built and trained with various holographic noisy phase data. The possibility of using a network pre-trained on natural images with Gaussian noise is also investigated. All models are evaluated in terms of phase error with HOLODEEP benchmark data and with three unseen images corresponding to different experimental conditions. The best results are obtained using a network with only four convolutional blocks and trained with a wide range of noisy phase patterns.

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