A comparative study on wavelet denoising for high noisy CT images of COVID-19 disease

Güngör, Murat Alparslan

doi:10.1016/j.ijleo.2021.166652

Cited by 17 publications

(8 citation statements)

References 18 publications

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“… Rajpal, Lakhyani, Singh, Kohli, and Kumar (2021) have used a deep convolutional neural network (ResNet-50) to learn the features from the chest X-ray images and distinguished among the three classes, namely, normal, COVID-19 and pneumonia. Gungor (2021) de-noised the CT images through DWT using various mother wavelets and diagnosed the COVID-19 disease. It was observed that, the second-level approximation coefficients of Daubechies (db3) wavelet have yielded better results among the other mother wavelets.…”

Section: Methodsmentioning

confidence: 99%

Wavelet and deep learning-based detection of SARS-nCoV from thoracic X-ray images for rapid and efficient testing

Verma

Inturi

Saurabh

et al. 2021

Expert Systems with Applications

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This paper proposes a wavelet and artificial intelligence-enabled rapid and efficient testing procedure for patients with Severe Acute Respiratory Coronavirus Syndrome (SARS-nCoV) through a deep learning approach from thoracic X-ray images. Presently, the virus infection is diagnosed primarily by a process called the real-time Reverse Transcriptase- Polymerase Chain Reaction (rRT-PCR) based on its genetic prints. This whole procedure takes a substantial amount of time to identify and diagnose the patients infected by the virus. The proposed research uses a wavelet-based convolution neural network architectures to detect SARS-nCoV. CNN is pre-trained on the ImageNet and trained end-to-end using thoracic X-ray images. To execute Discrete Wavelet Transforms (DWT), the available mother wavelet functions from different families, namely Haar, Daubechies, Symlet, Biorthogonal, Coiflet, and Discrete Meyer, were considered. Two-level decomposition via DWT is adopted to extract prominent features peripheral and subpleural ground-glass opacities, often in the lower lobes explicitly from thoracic X-ray images to suppress noise effect, further enhancing the signal to noise ratio. The proposed wavelet-based deep learning models of both, two-class instances (COVID vs. Normal) and four-class instances (COVID-19 vs. PNA bacterial vs. PNA viral vs. Normal) were validated from publicly available databases using k-Fold Cross Validation (k-Fold CV) technique. In addition to these X-ray images, images of recent COVID-19 patients were further used to examine the model’s practicality and real-time feasibility in combating the current pandemic situation. It was observed that the Symlet 7 approximation component with two-level manifested the highest test accuracy of 98.87%, followed by Biorthogonal 2.6 with an efficiency of 98.73%. While the test accuracy for Symlet 7 and Biorthogonal 2.6 is high, Haar and Daubechies with two levels have demonstrated excellent validation accuracy on unseen data. It was also observed that the precision, the recall rate, and the dice similarity coefficient for four-class instances were 98%, 98%, and 99%, respectively, using the proposed algorithm.

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Section: Methodsmentioning

confidence: 99%

Wavelet and deep learning-based detection of SARS-nCoV from thoracic X-ray images for rapid and efficient testing

Verma

Inturi

Saurabh

et al. 2021

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…The ACF algorithm for exact thresholding can be found in Ref. [5,6] . On the other hand, the thresholding function determines the way in which wavelet detail coefficients below a predefined…”

Section: 、 Principlementioning

confidence: 99%

Improved wavelet thresholding function for multi-mode fiber micro-vibration signal processing

Feng,

Chen,

et al. 2023

AOPC 2023: Optic Fiber Gyro

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Wavelet transform can suppress the noise of multimode fiber optic micro-vibration sensing signal, but still seriously affected by the existing threshold function. In this paper, we proposed an improved wavelet threshold function based upon hyperbolic tangent function to perform wavelet denoising on heartbeat signals to verify its effectiveness. The results show that under the input signal-to-noise ratio (SNR) of 5dB, the output SNR of each decomposition layer obtained by the improved threshold function is higher than those of the existing methods, while the root mean square error (RMSE) is smaller. It is worth noting that when the signal is cleaner, less noise can be denoise but higher SNR can be achieved, indicating that the proposed threshold function greatly alleviates the pseudo-Gibbs and constant bias. This research improves multimode fiber micro-vibration sensing performance and facilitates health diagnostics.

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“…Median filtering is suitable for processing images with random noise, and the amount of information loss is less than that of mean filtering. 7,8 Wiener filtering, 9 however, needs to find an ideal image before denoising. It minimizes the estimated value difference between the ideal image and the image to be processed.…”

Section: Introductionmentioning

confidence: 99%

“…Median filtering sorts all pixel values in the mask ascendingly and assigns the median value to the center pixel. Median filtering is suitable for processing images with random noise, and the amount of information loss is less than that of mean filtering 7,8 . Wiener filtering, 9 however, needs to find an ideal image before denoising.…”

Section: Introductionmentioning

confidence: 99%

Improved sparse representation algorithm for optical coherence tomography images

Miao

Zhou

Wang³

et al. 2022

Int J Imaging Syst Tech

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An improved sparse representation algorithm is proposed for reducing speckle noise in optical coherence tomography (OCT) images. To reduce the dictionary training time, the orthogonal matching pursuit algorithm (OMP) is replaced by a piecewise orthogonal matching pursuit algorithm (ST-OMP), and a threshold standard is set to train multiple atoms simultaneously to improve the tracking efficiency. Normal, diabetic macular edema, and fundus neovascularization membrane retina OCT images were tested using the proposed algorithm and other denoising algorithms. The results show that the proposed algorithm exhibits an excellent denoising ability. The details and edge information in the OCT images were well preserved by the proposed algorithm. Meanwhile, the image processing time was reduced by half with the proposed algorithm compared with the traditional K-SVD algorithm.

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A comparative study on wavelet denoising for high noisy CT images of COVID-19 disease

Cited by 17 publications

References 18 publications

Wavelet and deep learning-based detection of SARS-nCoV from thoracic X-ray images for rapid and efficient testing

Wavelet and deep learning-based detection of SARS-nCoV from thoracic X-ray images for rapid and efficient testing

Improved wavelet thresholding function for multi-mode fiber micro-vibration signal processing

Improved sparse representation algorithm for optical coherence tomography images

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