Comparative analysis of wavelet transform filtering systems for noise reduction in ultrasound images

Vilímek, Dominik; Kubíček, Jan; Golian, Milos; Jaroš, René; Kahánková, Radana; Hanzlíková, Pavla; Barvik, Daniel; Krestanova, Alice; Penhaker, Marek; Černý, Martin; Prokop, Ondrej; Bužga, Marek

doi:10.1371/journal.pone.0270745

Cited by 14 publications

(6 citation statements)

References 57 publications

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“…The symbol "∑" means addition. PSNR [41]; 𝑃𝑆𝑁𝑅 = 10 𝑙𝑜𝑔 10 255 2 𝑀𝑆𝐸 (10) Here, 255 is the maximum pixel value of the image, and MSE is the mean square error. When the results given in Tables 4 and 5 are examined, the effect of applying morphological processing on edge detection was observed in both Canny and Sobel algorithms.…”

Section: Resultsmentioning

confidence: 99%

Machine Whell Edge Detection Morphological Operations

KARAKUŞ

2024

KONJES

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One of the critical issues of image processing, defined as obtaining useful information from the image and improving the quality of the image, is edge detection. How edge detection performance will be affected by adding morphological operators to edge detection algorithms is among the issues that have not been fully resolved. In the study, Canny and Sobel edge detection algorithms were applied to different milling cutters used in machinability. Morphological operators were applied to the determined edges, and their effects on the edges were examined. Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR) values were used to compare the performances of edge detection algorithms. According to MSE and PSNR results, it was seen that the Canny algorithm gave better results than the Sobel algorithm. In addition, it was concluded that the images obtained as a result of the applied morphological operations provided better performance than the images that were not applied for both Canny and Sobel algorithms.

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Section: Resultsmentioning

confidence: 99%

Machine Whell Edge Detection Morphological Operations

KARAKUŞ

2024

KONJES

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“…Drawing parallels between wavelet denoising and grid search provided a comprehensive framework. As we measured the quality of a denoised signal using metrics such as energy preservation ( E ) in eq , mean squared error (MSE) in eq , and peak SNR (PSNR) in eq , − we were reminded of how wavelet parameter performance was quantified. Through this lens, our commitment to isolating precise CH 4 signature signals amidst the persistent challenges of system noise became evident. E = ∑ t = 1 N false| x false( t false) false| 2 where x ( t ) represents the signal amplitude at time t , and N is the total sample count. M S E = 1 N ∑ t = 1 N false[ x ( t ) − x̂ ( t ) false] 2 where x ( t ) and x̂ ( t ) are the original and denoised signals at time t , respectively. P S N R = 10 · log 10 ( MAX 2 M S E ) where MAX is the peak amplitude of the original signal.…”

Section: Methodsmentioning

confidence: 99%

Experimental Study on Enhanced Methane Detection Using an MEMS-Pyroelectric Sensor Integrated with a Wavelet Algorithm

Dong,

Sugai,

Wang

et al. 2024

ACS Omega

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An optical sensing approach that balances portability with cost efficiency has been designed for the reliable monitoring of fugitive methane (CH 4 ) emissions. Employing a LiTaO 3 -based pyroelectric detector integrated with micro-electromechanical systems and a broad infrared source, the developed gas sensor adeptly measured CH 4 concentrations with a low limit of detection of about 5.6 ppm v and showed rapid response times with t 90 consistently under 3 s. Notably, the novelty of our method lies in its precise control and reduction of CH 4 levels, enhanced by wavelet denoising. This technique, optimized through meticulous grid search, effectively mitigated noise interference noticeable at CH 4 levels below 10 ppm v . Postdenoising, nonlinear regression analyses based on the modified Beer−Lambert equation returned R 2 values of 0.985 and 0.982 for the training and validation sets, respectively. In conclusion, this gas sensor has been shown to be able to meet the requirements for early warning of CH 4 leakage on the surface in various carbon capture, utilization, and storage projects such as enhanced oil or gas recovery projects using CO 2 injection.

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“…For signal decomposition, it is important to make a good choice of the mother wavelet or analysis wavelet. This wavelet must possess the following properties: symmetry, orthogonality, and suitability for discrete wavelet transform (DWT), [11][12][13][14]. A group of mother wavelets has been tested, including the Haar wavelet, Daubechies wavelet, Symlet wavelet, and Coiflet wavelet.…”

Section: Denoising Of Sample Md6 Influence Of Wavelet Typesmentioning

confidence: 99%

Effectiveness of Wavelet Denoising on Secondary Ion Mass Spectrometry Signals

Dahraoui,

Boulakroune,

Khelfaoui

et al. 2023

East Eur. J. Phys.

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Wavelet theory has already achieved huge success. For Secondary Ions Mass Spectrometry (SIMS) signals, denoising the secondary signal, which is altered by the measurement, is considered that an essential step prior to applying such a signal processing technique that aims enhance the SIMS signals.The most efficient and widely used wavelet denoising method is based on wavelet coefficient thresholding. This process involves three important steps; wavelet decomposition: the input signals are decomposed into wavelet coefficients, thresholding: the wavelet coefficients are modified according to a threshold, and reconstruction: the modified coefficients are used in an inverse transform to obtain the noise-free-signal. Several researchers have used thresholding wavelet denoising techniques. The choice of wavelet type and the level of resolution can have a significant influence; it is important to note that the choice of resolution level depends on the type of signal we are dealing with, the nature of the present noise, and our specific goals for the denoised signal. It is generally recommended to test different resolution levels and evaluate their impact on the quality of the denoised signal before making a final decision. Moreover, the results obtained in wavelet denoising can be significantly influenced by the selection of wavelet types. The chosen wavelet type plays a crucial role in the extraction of signal details. Indeed, the effectiveness of denoising the MD6 sample has been demonstrated by the results obtained with sym4, db8, Haar and coif5 wavelets? These wavelets have effectively reduced noise while preserving crucial signal information, leading to an enhancement in the quality of the denoised signal.

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Comparative analysis of wavelet transform filtering systems for noise reduction in ultrasound images

Cited by 14 publications

References 57 publications

Machine Whell Edge Detection Morphological Operations

Machine Whell Edge Detection Morphological Operations

Experimental Study on Enhanced Methane Detection Using an MEMS-Pyroelectric Sensor Integrated with a Wavelet Algorithm

Effectiveness of Wavelet Denoising on Secondary Ion Mass Spectrometry Signals

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