Complex Wavelet Transform Research Articles

Automatic Diagnosis of Epileptic Seizures in EEG Signals Using Fractal Dimension Features and Convolutional Autoencoder Method

This paper proposes a new method for epileptic seizure detection in electroencephalography (EEG) signals using nonlinear features based on fractal dimension (FD) and a deep learning (DL) model. Firstly, Bonn and Freiburg datasets were used to perform experiments. The Bonn dataset consists of binary and multi-class classification problems, and the Freiburg dataset consists of two-class EEG classification problems. In the preprocessing step, all datasets were prepossessed using a Butterworth band pass filter with 0.5–60 Hz cut-off frequency. Then, the EEG signals of the datasets were segmented into different time windows. In this section, dual-tree complex wavelet transform (DT-CWT) was used to decompose the EEG signals into the different sub-bands. In the following section, in order to feature extraction, various FD techniques were used, including Higuchi (HFD), Katz (KFD), Petrosian (PFD), Hurst exponent (HE), detrended fluctuation analysis (DFA), Sevcik, box counting (BC), multiresolution box-counting (MBC), Margaos-Sun (MSFD), multifractal DFA (MF-DFA), and recurrence quantification analysis (RQA). In the next step, the minimum redundancy maximum relevance (mRMR) technique was used for feature selection. Finally, the k-nearest neighbors (KNN), support vector machine (SVM), and convolutional autoencoder (CNN-AE) were used for the classification step. In the classification step, the K-fold cross-validation with k = 10 was employed to demonstrate the effectiveness of the classifier methods. The experiment results show that the proposed CNN-AE method achieved an accuracy of 99.736% and 99.176% for the Bonn and Freiburg datasets, respectively.

Image Representation Method Based on Complex Wavelet Transform and Phase Congruency, with Automatic Threshold Selection

Image representation is an active area of research with increasing applications in military and defense. Image representation aims at representing an image with lesser number of coefficients than the actual image, without affecting the image quality. It is the first step in image compression. Once the image is represented by using some set of coefficients, it is further encoded using various compression algorithms. This paper proposes an adaptive method for image representation, which uses Complex Wavelet transform and the concept of phase congruency, where the number of coefficients used for image representation depends on the information content in the input image. The efficiency of the proposed method has been assessed by comparing the number of coefficients used to represent the image using the proposed method with that used when Complex Wavelet transform is used for image representation. The resultant image quality is determined by computing the PSNR values and Normalized Cross Correlation. Experiments carried out show highly promising results, in terms of the reduction in the number of coefficients used for image representation and the quality of the resultant image.

Medical Image Enhancement through Dual Tree Complex Wavelet Transform and Multi-resolution Gabor Filter

Medical image processing is essential in the field of clinical medicine for analysis and diagnosis of diseases. Most of these medical images are acquired under low illumination and noisy environment. Hence it produces a bottleneck in the processing of medical images through computer vision system. Due to tremendous details and complexity of the medical images, image enhancement is challenging and difficult task. In this paper, medical image enhancement through dual tree complex wavelet transform (DTCWT) and multi-resolution Gabor filter is proposed. After the medical images are acquired using standard techniques, DTCWT is applied that decomposes the gray scale image into high and low frequency subbands. Further DTCWT is applied twice on low frequency subbands to decompose into its high and low frequency subbands. Then multiresolution Gabor filter is applied to decomposed six high frequency subands and two low frequency subband to obtain enhancement in multiple directions. Finally inverse DTCWT is applied to obtain enhanced medical image. The experimental results demonstrate that the approach based on DTCWT and multi-resolution Gabor filter can produce better image enhancement as compared with the conventional and latest methods. The proposed medical image enhancement technique can be applied in analysis and diagnosis of diseases through computer vision system.

Automatic Electrophysiological Noise Reduction and Epileptic Seizure Detection for Stereoelectroencephalography.

The objective of this study was to develop a computational algorithm capable of locating artifacts and identifying epileptic seizures, which specifically implementing in clinical stereoelectroencephalography (SEEG) recordings. Based on the nonstationary nature and broadband features of SEEG signals, a comprehensive strategy combined with the complex wavelet transform (CWT) and multi-layer thresholding method was implemented for both noise reduction and seizure detection. The artifacts removal pipeline integrated edge artifact removal, discrete spectrum analysis, and peak density evaluation. For automatic seizure detection, integrated power analysis and multi-dynamic thresholding were applied. The F1score was applied to evaluate overall performance of the algorithm. The algorithm was tested using expert-marked, double-blinded, clinical SEEG data from seven patients undergoing presurgical evaluation. This approach achieved the F1 score of 0.86 for noise reduction and 0.88 for seizure detection. This offline-approach method with minimum parameter tuning procedures and no prior information required, proved to be a feasible and solid solution for clinical SEEG data evaluation. Moreover, the algorithm can be improved with additional tuning and implemented with machine learning postprocessing pipelines.

Extended and optimized deep convolutional neural network‐based lung tumor identification in big data

AbstractLung tumor is a complex disease caused due to the irregular growth of lung cells. A key factor in effective treatment planning is the early detection of lung tumor. Visual similarity between benign and malignant nodules, heterogeneity and low contrast variation are the factors that make accurate cancerous lesion recognition, a very challenging task. In this paper, Optimized Deep convolutional neural network (DCNN) and Fuzzy C‐means with Equilibrium optimizer (FCM‐EO) is proposed for classification and segmentation of CT lung images. The proposed architecture is comprised of four phases such as preprocessing, feature extraction, classification and segmentation. In preprocessing, the weighted mean histogram analysis (WMHA) is utilized to enhance the quality of images and noise removal. Hybrid Dual tree‐complex wavelet transform (DT‐CWT) with Gabor filter is proposed in feature extraction to extract the features from the preprocessed images. DCNN model is designed to classify the original images into benign and malignant images. The weight of DCNN model is updated using the Enhanced black widow optimization algorithm (EBWOA). In segmentation, FCM‐EO is introduced to identify the tumor regions and remove the outliers from the malignant images. LIDC‐IDRI dataset is utilized for the experimental analysis and MATLAB is the implementation tool. The simulation analysis is performed for both the classification and segmentation processes. Accuracy, specificity, sensitivity, precision, F‐measure, FROC, DSC, MCC, and IoU are evaluated for both these processes. The experimental results showed the proposed framework is efficient for the identification of tumor from the CT lung images.

Lifting dual tree complex wavelets transform

We describe the lifting dual tree complex wavelet transform (LDTCWT), a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). We describe a way to estimate the accuracy of this approximation and style appropriate filters to attain this. These benefits are often exploited among applications like denoising, segmentation, image fusion and compression. The results of applications shrinkage denoising demonstrate objective and subjective enhancements over the dual tree complex wavelet transform (DTCWT). The results of the shrinkage denoising example application indicate empirical and subjective enhancements over the DTCWT. The new transform with the DTCWT provide a trade-off between denoising computational competence of performance, and memory necessities. We tend to use the PSNR (peak signal to noise ratio) alongside the structural similarity index measure (SSIM) and the SSIM map to estimate denoised image quality.

DEPSO With DTCWT Algorithm for Multimodal Medical Image Fusion

In this paper, the authors propose an algorithm of hybrid particle swarm with differential evolution (DE) operator, termed DEPSO, with the help of a multi-resolution transform named dual tree complex wavelet transform (DTCWT) to solve the problem of multimodal medical image fusion. This hybridizing approach aims to combine algorithms in a judicious manner, where the resulting algorithm will contain the positive features of these different algorithms. This new algorithm decomposes the source images into high-frequency and low-frequency coefficients by the DTCWT, then adopts the absolute maximum method to fuse high-frequency coefficients; the low-frequency coefficients are fused by a weighted average method while the weights are estimated and enhanced by an optimization method to gain optimal results. The authors demonstrate by the experiments that this algorithm, besides its simplicity, provides a robust and efficient way to fuse multimodal medical images compared to existing wavelet transform-based image fusion algorithms.

Feature Extraction of Tea Leaf Images using Dual-Tree Complex Wavelet Transform and Gray Level Co-occurrence Matrix

Feature extraction is a very important part of machine learning to analyze and find relationships between objects of different categories. This paper aims to analyze the feature vectors of the tea leaf image produced by using a combination of dual-tree complex wavelet transform and gray level co-occurrence matrix techniques. The tea leaf image consists of four different categories, each representing different phases of tea growth and were acquired using a visible camera from eight different orientations. Feature extraction using Principle Component Analysis (PCA) shows that the texture features can identify a different category of the leaf images without being significantly affected by the difference in scale and orientation of the images.

Image Focus Enhancement Using Focusing Filter and DT-CWT Based Image Fusion

Combining multi-model images of the same scene that have different focus distances can produce clearer and sharper images with a larger depth of field. Most available image fusion algorithms are superior in results. However, they did not take into account the focus of the image. In this paper a fusion method is proposed to increase the focus of the fused image and to achieve highest quality image using the suggested focusing filter and Dual Tree-Complex Wavelet Transform. The focusing filter consist of a combination of two filters, which are Wiener filter and a sharpening filter. This filter is used before the fusion operation using Dual Tree-Complex Wavelet Transform. The common fusion rules, which are the average-fusion rule and maximum-fusion rule, were used to obtain the fused image. In the experiment, using the focus operators, the performance of the proposed fusion algorithm was compared with the existing algorithms. The results showed that the proposed method is better than these fusion methods in terms of the focus and quality.

A Novel Zero Watermarking Based on DT-CWT and Quaternion for HDR Image

This paper presents a high dynamic range (HDR) image zero watermarking method based on dual tree complex wavelet transform (DT-CWT) and quaternion. In order to be against tone mapping (TM), DT-CWT is used to transform the three RGB color channels of the HDR image for obtaining the low-pass sub-bands, respectively, since DT-CWT can extract the contour of the HDR image and the contour change of the HDR image is small after TM. The HDR image provides a wide dynamic range, and thus, three-color channel correlations are higher than inner-relationships and the quaternion is used to consider three color channels as a whole to be transformed. Quaternion fast Fourier transform (QFFT) and quaternion singular value decomposition (QSVD) are utilized to decompose the HDR image for obtaining robust features, which is fused with a binary watermark to generate a zero watermark for copyright protection. Furthermore, the binary watermark is scrambled for the security by using the Arnold transform. Experimental results denote that the proposed zero-watermarking method is robust to TM and other image processing attacks, and can protect the HDR image efficiently.

Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain

In recent years, there has been an increasing research interest in image de-noising due to an emphasis on sparse representation. When sparse representation theory is compared to transform domain-based image de-noising, the former indicates that the images have more information. It contains structural characteristics that are quite similar to the structure of dictionary-based atoms. This structure and the dictionary-based method is highly unsuccessful. However, image representation assumes that the noise lack such a feature. The dual-tree complex wavelet transform incorporates an increase in transform data density to reduce the effects of sparse data. This technique has been developed to decrease the image noise by selecting the best-predicted threshold value derived from wavelet coefficients. For our experiment, Discrete Cosine Transform (DCT) and Complex Wavelet Transform (CWT) are used to examine how the suggested technique compares the conventional DCT and CWT on sets of realistic images. As for image quality measures, DT-CWT has leveraged superior results. In terms of processing time, DT-CWT gave better results with a wider PSNR range. Further, the proposed model is tested with a standard digital image named Lena and multimedia sensor images for the denoising algorithm. The suggested denoising technique has delivered minimal effect on the MSE value.

Pap smear based cervical cancer detection using residual neural networks deep learning architecture

AbstractDetecting and classifying the Pap smear cell images is important task for cervical cancer identification. In this article, dual tree complex wavelet transform (DTCWT) based modified deep learning algorithm is proposed for classifying the Pap smear cell images into four different cases normal, carcinoma in situ, dysplastic, and superficial. This proposed work consists of data augmentation module, DTCWT module and convolutional neural networks (CNN) module for the automatic classification of Pap smear cell images. The CNN module required huge number of cell images for obtaining high classification rate. Hence, the shearing and flipping functions are used in the data augmentation process to improve the dataset samples for further CNN classification module. Then, the spatial pixel behavior of data augmented images is transferred into multimodal pixels using DTCWT, which creates the sub band coefficients in matrix format. This matrix is trained and classified using ResNet 18 which classifies the source Pap smear cell image into four classes. This developed method is tested on the Pap smear cell images available in open access dataset. The average Pap smear detection index (PDI) of the automatic Pap smear cell image classification system is about 99%.

Prediction of Drug-Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method.

Identification of drug–target interactions (DTIs) is vital for drug discovery. However, traditional biological approaches have some unavoidable shortcomings, such as being time consuming and expensive. Therefore, there is an urgent need to develop novel and effective computational methods to predict DTIs in order to shorten the development cycles of new drugs. In this study, we present a novel computational approach to identify DTIs, which uses protein sequence information and the dual-tree complex wavelet transform (DTCWT). More specifically, a position-specific scoring matrix (PSSM) was performed on the target protein sequence to obtain its evolutionary information. Then, DTCWT was used to extract representative features from the PSSM, which were then combined with the drug fingerprint features to form the feature descriptors. Finally, these descriptors were sent to the Rotation Forest (RoF) model for classification. A 5-fold cross validation (CV) was adopted on four datasets (Enzyme, Ion Channel, GPCRs (G-protein-coupled receptors), and NRs (Nuclear Receptors)) to validate the proposed model; our method yielded high average accuracies of 89.21%, 85.49%, 81.02%, and 74.44%, respectively. To further verify the performance of our model, we compared the RoF classifier with two state-of-the-art algorithms: the support vector machine (SVM) and the k-nearest neighbor (KNN) classifier. We also compared it with some other published methods. Moreover, the prediction results for the independent dataset further indicated that our method is effective for predicting potential DTIs. Thus, we believe that our method is suitable for facilitating drug discovery and development.

Emboli detection using a wrapper-based feature selection algorithm with multiple classifiers

Traditionally, analyzing spectral recordings and Doppler shift sounds for detecting emboli is done by experts visually and aurally. These techniques for detecting emboli are both subjective and expensive. In the proposed study, an emboli detection system, which makes binary classification to decide whether a signal is emboli or not, is developed using Q-factor tuned Directional Dual-Tree Rational-Dilation Complex Wavelet Transform for feature extraction. A Doppler ultrasound signal dataset including 400 samples – 200 from embolic, 100 from speckles, and 100 from artifacts – is used. Besides feeding dataset to Support Vector Machines, Multilayer Perceptron, Logistic Regression algorithms for classification; the ensemble voting approach was also applied to obtain higher performance. The experiments including the feature selection and classification algorithms are conducted with an unbiased two-step cross-validation procedure. Firstly, grid-search was used for finding optimum hyper-parameters and features on the training and validation sets. Next, the optimal model was applied to the test set. Lastly, a wrapper-based feature selection algorithm called Boruta was applied to the dataset to overcome insufficient number of samples problem. This problem is very common in biomedical studies due to the difficulties occurring in their creation such as the high-dependency to well-trained human power to acquire meaningful data, and expensiveness in terms of money and time. The results showed that ensemble learning has higher performance than single classifiers when a limited number of training samples is available. Besides, the results point out that close prediction performance was obtained with fewer samples when the Boruta algorithm was applied.

Optimizing channel selection using multi-objective FODPSO for BCI applications

ABSTRACT Brain-computer interfacing (BCI) requires multichannel electroencephalogram (EEG) or electrocorticogram (ECoG) signal acquisition for better performance. The selection of optimum channels that provides the best accuracy is itself a problem to solve because noisy/irrelevant channels can complexify the system and degrade its performance. This paper presents a novel automated model for optimum channel selection for BCI applications using the Fractional Order Darwinian Particle Swarm Optimization (FODPSO). To assess the information content of selected channels, a Support Vector Machine (SVM) classifier is used. The weighted sum of the number of channels and the classification accuracy on validation samples is taken as the fitness value for the FODPSO based binary optimization method (where all the variables are binary). The FODPSO and SVM based algorithm is evaluated on the electrocorticography (ECoG) recordings that have been used in BCI competition III. Dual tree complex wavelet transform (DTCWT) is used for preprocessing of the data and sample entropy is calculated for obtaining the most informative features from the preprocessed data. Eight channels are selected using this algorithm, yielding a classification accuracy of 0.81 for the testing dataset (classifying the ECoG signals of imagined movement of little finger and tongue) that compares favorably with the already reported methods. Experimental results successfully demonstrate that this channel selection algorithm works better both in terms of classification accuracy and in the reduction of the number of required channels.

High Speed Data Exchange Algorithm in Telemedicine with Wavelet based on 4D Medical Image Compression

Existing Medical imaging techniques such as fMRI, positron emission tomography (PET), dynamic 3D ultrasound and dynamic computerized tomography yield large amounts of four-dimensional sets. 4D medical data sets are the series of volumetric images netted in time, large in size and demand a great of assets for storage and transmission. Here, in this paper, we present a method wherein 3D image is taken and Discrete Wavelet Transform(DWT) and Dual-Tree Complex Wavelet Transform(DTCWT) techniques are applied separately on it and the image is split into sub-bands. The encoding and decoding are done using 3D-SPIHT, at different bit per pixels(bpp). The reconstructed image is synthesized using Inverse DWT technique. The quality of the compressed image has been evaluated using some factors such as Mean Square Error(MSE) and Peak-Signal to Noise Ratio (PSNR).

Image Denoising Based on Dual-tree Complex Wavelet Transform and Convolutional Neural Network

In view of the problem that the noise in the image will have a negative impact on the subsequent image processing, this paper proposes a noise reduction algorithm based on the dual-tree complex wavelet transform and convolutional neural network by using the characteristics of the DTCWT and CNN. Firstly, the two-dimensional image is decomposed into two parallel wavelet trees by double tree complex wavelet transform, which forms six high-frequency detail parts and two same low-frequency parts in different directions. Then, it is put into the designed deep convolution neural network for training, Finally, the six high-frequency components and one of the low-frequency components are recombined into the image after noise reduction by using the inverse transform of double tree complex wavelet. The simulation results show that PSNR and MS-SSIM of the image are better than other noise reduction algorithms, and the details of the original image can be restored well while noise reduction. It has practical application value in the case that the collected image has a large noise due to various reasons.

Deep Learning-Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images.

Patients infected with the COVID-19 virus develop severe pneumonia, which generally leads to death. Radiological evidence has demonstrated that the disease causes interstitial involvement in the lungs and lung opacities, as well as bilateral ground-glass opacities and patchy opacities. In this study, new pipeline suggestions are presented, and their performance is tested to decrease the number of false-negative (FN), false-positive (FP), and total misclassified images (FN + FP) in the diagnosis of COVID-19 (COVID-19/non-COVID-19 and COVID-19 pneumonia/other pneumonia) from CT lung images. A total of 4320 CT lung images, of which 2554 were related to COVID-19 and 1766 to non-COVID-19, were used for the test procedures in COVID-19 and non-COVID-19 classifications. Similarly, a total of 3801 CT lung images, of which 2554 were related to COVID-19 pneumonia and 1247 to other pneumonia, were used for the test procedures in COVID-19 pneumonia and other pneumonia classifications. A 24-layer convolutional neural network (CNN) architecture was used for the classification processes. Within the scope of this study, the results of two experiments were obtained by using CT lung images with and without local binary pattern (LBP) application, and sub-band images were obtained by applying dual-tree complex wavelet transform (DT-CWT) to these images. Next, new classification results were calculated from these two results by using the five pipeline approaches presented in this study. For COVID-19 and non-COVID-19 classification, the highest sensitivity, specificity, accuracy, F-1, and AUC values obtained without using pipeline approaches were 0.9676, 0.9181, 0.9456, 0.9545, and 0.9890, respectively; using pipeline approaches, the values were 0.9832, 0.9622, 0.9577, 0.9642, and 0.9923, respectively. For COVID-19 pneumonia/other pneumonia classification, the highest sensitivity, specificity, accuracy, F-1, and AUC values obtained without using pipeline approaches were 0.9615, 0.7270, 0.8846, 0.9180, and 0.9370, respectively; using pipeline approaches, the values were 0.9915, 0.8140, 0.9071, 0.9327, and 0.9615, respectively. The results of this study show that classification success can be increased by reducing the time to obtain per-image results through using the proposed pipeline approaches.

A Novel Video Watermarking Algorithm using DTCWT

In this paper a new novel color video watermarking algorithm has been proposed using Dual Tree Complex Wavelet Transform (DTCWT). Digital watermarking is an emerging technology for copyright protection for digital multimedia data. Now-a-days a lot of digital data are exchanged in the internet, so to protect the digital multimedia data, digital watermarking is used, the data may be an image or video or an audio data. The main objective of this paper is to maintain perceptivity of digital video and design a robust watermarking algorithm and maintaining the tradeoff between robustness and perceptivity.

Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform

This paper presents a new content-based image retrieval (CBIR) method based on image feature fusion. The deep features are extracted from object-centric and place-centric deep networks. The discrete cosine transform (DCT) solves the strong correlation of deep features and reduces dimensions. The shallow features are extracted from a Quantized Uniform Local Binary Pattern (ULBP), hue-saturation-value (HSV) histogram, and dual-tree complex wavelet transform (DTCWT). Singular value decomposition (SVD) is applied to reduce the dimensions of ULBP and DTCWT features. The experimental results tested on Corel datasets and the Oxford building dataset show that the proposed method based on shallow features fusion can significantly improve performance compared to using a single type of shallow feature. The proposed method based on deep features fusion can slightly improve performance compared to using a single type of deep feature. This paper also tests variable factors that affect image retrieval performance, such as using principal component analysis (PCA) instead of DCT. The DCT can be used for dimensional feature reduction without losing too much performance.