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 Ultrasound Image Denoising using Multiscale Ridgelet Transform with Hard and NeighCoeff Thresholding

2013 ◽  
pp. 150-155
Author(s):  
Preeti Goel ◽  
H. P. Sinha ◽  
Harpreet Singh
Keyword(s):  
Adaptive Filters ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
The Body ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Sound Waves ◽  
Edge Preservation ◽  
Ridgelet Transform ◽  
Visual Enhancement

Ultrasound imaging utilizes sound waves reflected from different organs of the body to give local details and important diagnostic information on the human body. However, using ultrasound images for diagnosis is difficult because of the existence of speckle noise in the image. The speckle noise is due to interference between coherent waves which are backscattered by targeted surfaces and arrive out of phase at the sensor. This hampers the perception and the extraction of fine details from the image. Speckle reduction/filtering i.e. visual enhancement techniques are used for enhancing the visual quality of the images. The multscale ridgelet transform based denoising algorithm for Ultrasound images is proposed for effective edge preservation in comparison to filtering techniques using the Adaptive Filters.

Despeckling of Ultrasound Images in Contourlet Domain

2013 ◽  
Vol 647 ◽  
pp. 283-287
Author(s):  
Yu Shu Liu ◽  
Ming Yan Jiang
Keyword(s):  
Edge Detection ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Image Feature ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Edge Preservation ◽  
Directional Information ◽  
Relative Variance ◽  
Disease Diagnostics

Ultrasound images are the important foundation for disease diagnostics. Unfortunately, speckle noise is an inherent property of ultrasound images. So speckle reduction is an important pre-processing step in the ultrasound image feature extraction and analysis. This paper proposes a novel noise reduction algorithm for ultrasound images, which is based on edge detection of the images using the directional information of contourlet transform. The relative variance of the contourlet coefficients is used as a measure of edge detection. The adaptive threshold can be calculated using the probability density function of relative variance. It is shown that the proposed method outperforms several existing techniques in terms of the universal index, edge preservation and visual quality, and in addition, is able to maintain the significant details of ultrasound images.

scholarly journals MFNR: Multi-Frame Method for Complete Speckle Noise Removal in Ultrasound images Using KDE

2020 ◽  
Author(s):  
Awais Nazir ◽  
Muhammad Shahzad Younis ◽  
Muhammad Khurram Shahzad
Keyword(s):  
Signal To Noise Ratio ◽  
Low Cost ◽  
Speckle Noise ◽  
Noise Removal ◽  
General Purpose ◽  
Imaging Systems ◽  
Ultrasound Images ◽  
Frame Method ◽  
Probability Density Function Pdf ◽  
Removal System

Speckle noise is one of the most difficult noises to remove especially in medical applications. It is a nuisance in ultrasound imaging systems which is used in about half of all medical screening systems. Thus, noise removal is an important step in these systems, thereby creating reliable, automated, and potentially low cost systems. Herein, a generalized approach MFNR (Multi-Frame Noise Removal) is used, which is a complete Noise Removal system using KDE (Kernal Density Estimation). Any given type of noise can be removed if its probability density function (PDF) is known. Herein, we extracted the PDF parameters using KDE. Noise removal and detail preservation are not contrary to each other as the case in single-frame noise removal methods. Our results showed practically complete noise removal using MFNR algorithm compared to standard noise removal tools. The Peak Signal to Noise Ratio (PSNR) performance was used as a comparison metric. This paper is an extension to our previous paper where MFNR Algorithm was showed as a general purpose complete noise removal tool for all types of noises

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.

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).

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