“…Subsequently, the texture preserving guided filter is applied to reduce the inherent quantum noise ( Sprawls, 2018 ). The choice of the de-noising filter is based on our previous study ( Chandra & Verma, 2020a ).…”
Section: Methodsmentioning
confidence: 99%
“…The GLCM feature describes the spatial correlation among the pixel intensities in radiographic texture patterns along four distinct directions (i.e., ) whereas the HOG feature encodes the local shape/texture information. The selection of these statistical texture features is motivated by the fact that it can efficiently encode the natural texture patterns and is widely used in medical image analysis ( Chandra and Verma, 2020a , Chandra et al, 2020 , Santosh and Antani, 2018 , Vajda et al, 2018 ). Fig.…”
Section: Methodsmentioning
confidence: 99%
“…To compute the discriminative performance of the aforementioned features we have used the popular supervised classification algorithms: SVM (linear, radial bias function, polynomial) ( Chandra and Verma, 2020a , Vapnik, 1998 ), ANN ( Artificial Neural Network, 2013 ), KNN ( Han, Kamber, & Pei, 2012 ), NB ( Khatami et al, 2017 , Venegas-Barrera and Manjarrez, 2011 ), and DT ( Han, Kamber, & Pei, 2012 , Pantazi, Moshou, & Bochtis, 2020 ). These algorithms are very fast and are widely used in the literature for the classification of pulmonary diseases using CXR images ( Chandra and Verma, 2020a , Santosh and Antani, 2018 ). The selection of these classifiers is motivated by the fact that these algorithms can be efficiently trained using smaller datasets without compromising with the performance.…”
Section: Methodsmentioning
confidence: 99%
“…Chest X-Ray (CXR) is one of the important, non-invasive clinical adjuncts that play an essential role in the preliminary investigation of different pulmonary abnormalities ( Chandra & Verma, 2020 , Chandra and Verma, 2020a , Chandra et al, 2020 , Ke et al, 2019 ). It can act as an alternative screening modality for the detection of nCOVID-19 or to validate the related diagnosis, where the CXR images are interpreted by expert radiologists to look for infectious lesions associated with nCOVID-19.…”
Highlights
Proposed automatic COVID screening (ACoS) system for detection of infected patients.
Random image augmentation is applied to incorporate the variability in the images.
Applied hierarchical (two phase) classification to segregate three classes.
Majority vote based classifier ensemble is used to combine model’s prediction.
Proposed method show promising potential to detect nCOVID-19 infected patients.
“…Subsequently, the texture preserving guided filter is applied to reduce the inherent quantum noise ( Sprawls, 2018 ). The choice of the de-noising filter is based on our previous study ( Chandra & Verma, 2020a ).…”
Section: Methodsmentioning
confidence: 99%
“…The GLCM feature describes the spatial correlation among the pixel intensities in radiographic texture patterns along four distinct directions (i.e., ) whereas the HOG feature encodes the local shape/texture information. The selection of these statistical texture features is motivated by the fact that it can efficiently encode the natural texture patterns and is widely used in medical image analysis ( Chandra and Verma, 2020a , Chandra et al, 2020 , Santosh and Antani, 2018 , Vajda et al, 2018 ). Fig.…”
Section: Methodsmentioning
confidence: 99%
“…To compute the discriminative performance of the aforementioned features we have used the popular supervised classification algorithms: SVM (linear, radial bias function, polynomial) ( Chandra and Verma, 2020a , Vapnik, 1998 ), ANN ( Artificial Neural Network, 2013 ), KNN ( Han, Kamber, & Pei, 2012 ), NB ( Khatami et al, 2017 , Venegas-Barrera and Manjarrez, 2011 ), and DT ( Han, Kamber, & Pei, 2012 , Pantazi, Moshou, & Bochtis, 2020 ). These algorithms are very fast and are widely used in the literature for the classification of pulmonary diseases using CXR images ( Chandra and Verma, 2020a , Santosh and Antani, 2018 ). The selection of these classifiers is motivated by the fact that these algorithms can be efficiently trained using smaller datasets without compromising with the performance.…”
Section: Methodsmentioning
confidence: 99%
“…Chest X-Ray (CXR) is one of the important, non-invasive clinical adjuncts that play an essential role in the preliminary investigation of different pulmonary abnormalities ( Chandra & Verma, 2020 , Chandra and Verma, 2020a , Chandra et al, 2020 , Ke et al, 2019 ). It can act as an alternative screening modality for the detection of nCOVID-19 or to validate the related diagnosis, where the CXR images are interpreted by expert radiologists to look for infectious lesions associated with nCOVID-19.…”
Highlights
Proposed automatic COVID screening (ACoS) system for detection of infected patients.
Random image augmentation is applied to incorporate the variability in the images.
Applied hierarchical (two phase) classification to segregate three classes.
Majority vote based classifier ensemble is used to combine model’s prediction.
Proposed method show promising potential to detect nCOVID-19 infected patients.
“…Therefore, the smoothing methods are gaining a significant importance in the computer aided analysis. Among others, Additive White Gaussian Noise (AWGN), impulse noise (also called Salt and Pepper), quantisation noise, Poisson noise, and speckle noise are frequently discussed in the recent literature [ 1 , 8 , 9 ]. Considering different nature of individual types of the noise, we can deduce that individual smoothing methods may be differently sensitive and robust to each of the noise type.…”
Wavelet transformation is one of the most frequent procedures for data denoising, smoothing, decomposition, features extraction, and further related tasks. In order to perform such tasks, we need to select appropriate wavelet settings, including particular wavelet, decomposition level and other parameters, which form the wavelet transformation outputs. Selection of such parameters is a challenging area due to absence of versatile recommendation tools for suitable wavelet settings. In this paper, we propose a versatile recommendation system for prediction of suitable wavelet selection for data smoothing. The proposed system is aimed to generate spatial response matrix for selected wavelets and the decomposition levels. Such response enables the mapping of selected evaluation parameters, determining the efficacy of wavelet settings. The proposed system also enables tracking the dynamical noise influence in the context of Wavelet efficacy by using volumetric response. We provide testing on computed tomography (CT) and magnetic resonance (MR) image data and EMG signals mostly of musculoskeletal system to objectivise system usability for clinical data processing. The experimental testing is done by using evaluation parameters such is MSE (Mean Squared Error), ED (Euclidean distance) and Corr (Correlation index). We also provide the statistical analysis of the results based on Mann-Whitney test, which points out on statistically significant differences for individual Wavelets for the data corrupted with Salt and Pepper and Gaussian noise.
Background
Chest X‐ray (CXR) images are commonly used to show the internal structure of the human body without invasive intervention. The quality of CXR is an important factor as it affects the accuracy of a clinical diagnosis. Unfortunately, it is difficult to always get good quality CXR scans due to noises and scatters.
Purpose
Recently, wavelet directional CycleGAN (WavCycleGAN) has shown promising results in image restoration tasks by removing noise and artifacts without sacrificing high‐frequency components of the input image. Unfortunately, WavCycleGAN directly reconstructs wavelet directional images that require a wavelet transform in both the training and test phases, resulting in additional processing steps and unnatural artifacts originating from the wavelet domain image. In addition, WavCycleGAN can only process artifact‐related subbands, so it is difficult to apply WavCycleGAN when different levels of artifacts are present in all subbands. To address this, here we present a novel unsupervised CXR image restoration scheme with similar or even better artifact removal performance than WavCycleGAN in spite of wavelet transform being only applied in the training phase.
Methods
We introduce a novel wavelet subband discriminator which can be combined with CycleGAN or switchable CycleGAN, where wavelet transform is applied only in the training phase for discriminators to match the distribution of wavelet subband components. In our framework, the image restoration network can be still applied in the image domain to prevent unnatural artifacts of the wavelet domain image with the help of the image‐domain cycle‐consistency loss. In addition, using wavelet subband discriminator makes it possible to remove artifacts in all subbands by utilizing frequency‐specific wavelet subband discriminators.
Results
Through extensive experiments for noise and scatter removal in CXRs, we confirm that our method provides competitive performance compared to existing approaches without additional processing steps in the test phase. Furthermore, we show that our wavelet subband discriminator combined with the switchable CycleGAN can provide the flexibility by generating different levels of artifact removal.
Conclusions
The proposed wavelet subband discriminator can be combined with the existing CycleGAN or switchable CycleGAN structures to construct an efficient unsupervised CXR image reconstruction. The advantage of our wavelet subband discriminator‐based CXR image restoration is that, unlike traditional WavCycleGAN, it does not require any additional processing steps in the testing phase and does not generate unnatural artifacts originating from the wavelet domain image. We believe that our wavelet subband discriminator can be applied to various CXR image applications.
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