High Subbands Research Articles

Effective relax acquisition: a novel approach to classify relaxed state in alpha band EEG-based transformation

A relaxed state is essential for effective hypnotherapy, a crucial component of mental health treatments. During hypnotherapy sessions, neurologists rely on the patient’s relaxed state to introduce positive suggestions. While EEG is a widely recognized method for detecting human emotions, analyzing EEG data presents challenges due to its multi-channel, multi-band nature, leading to high-dimensional data. Furthermore, determining the onset of relaxation remains challenging for neurologists. This paper presents the Effective Relax Acquisition (ERA) method designed to identify the beginning of a relaxed state. ERA employs sub-band sampling within the Alpha band for the frequency domain and segments the data into four-period groups for the time domain analysis. Data enhancement strategies include using Window Length (WL) and Overlapping Shifting Windows (OSW) scenarios. Dimensionality reduction is achieved through Principal Component Analysis (PCA) by prioritizing the most significant eigenvector values. Our experimental results indicate that the relaxed state is predominantly observable in the high Alpha sub-band, particularly within the fourth period group. The ERA demonstrates high accuracy with a WL of 3 s and OSW of 0.25 s using the KNN classifier (90.63%). These findings validate the effectiveness of ERA in accurately identifying relaxed states while managing the complexity of EEG data.Graphical abstract

A novel framework of multimodal medical image fusion using adaptive NSST and optimized deep learning approach

ABSTRACT Multimodal medical image fusion plays a pivotal role in the medical and imaging industry. Existing works of deep learning method suffers from blurred texture characteristics and computing efficiency. Thus, a novel deep learning model is proposed for multimodal medical image fusion. Initially, an Adaptive Non-Subsampled Shearlet Transform (ANSST) approach is developed for decomposing the images, where the filter coefficient is optimized by Hybrid Water Strider-Dingo Optimization (HWS-DOX). The fusion of the high sub-bands of source image 1 and high sub-bands of source image 2 is done by an Optimized Deep Neural Network (ODNN). Then, the lower sub-bands of source image 1 and lower sub-bands of source image 2 will be fused through a weighted averaging scheme. Finally, the final fused images are attained by applying the inverse ANSST. Thus, the performance of recommended module is compared over classical heuristics and various transform methods.

3-D OBJECT RECOGNITION USING MULTI-WAVELET AND NEURAL NETWORK

This search has introduced the techniques of multi-wavelet transform and neural network for recognition 3-D object from 2-D image using patches. The proposed techniques were tested on database of different patches features and the high energy subband of discrete multi-wavelet transform DMWT (gp) of the patches. The test set has two groups, group (1) which contains images, their (gp) patches and patches features of the same images as a part of that in the data set beside other images, (gp) patches and features, and group (2) which contains the (gp) patches and patches features the same as a part of that in the database but after modification such as rotation, scaling and translation. Recognition by back propagation (BP) neural network as compared with matching by minimum distance, gave (94%) and (83%) score by using group (1), (gp) and features respectively, which is much better than the minimum distance. Recognition using (gp) neural network (NN) gave a (94%) and (72%) score by using group (2), (gp) and features respectively, while the minimum distance gave (11%) and (33%) scores. Time consumptionthrough the recognition process using (NN) with (gp) is less than that minimum distance.

Hurst entropy profiles for Beta Low and Beta High EEG sub-bands Part I: Intragroup comparison.

The non-linear exploration of the entropic structure of the information contained in an EEG allows access to a new source of data that characterizes, in a more global way, the functioning of the brain as a whole. On the understanding that the brain is a hyper-complex system, describing it only through a linear statistical approach, mainly based on the comparison of differences in the magnitude of the power of the electroencephalographic signal between comparable conditions, leaves incomplete the part of the brain phenomenon that obeys non-linear laws. The present work explores the possibility of characterizing the entropic structure of a human brain, through the construction of order/chaos balance profiles. We proceeded to study the entropic content of the EEG signal, by estimating the Hurst exponent in segments of different sizes, for a set of data from 14 EEG electrodes (Emotiv Epoc Research Edition), taken in basal resting conditions, during 3-5 minutes with eyes closed. The EEG recordings of thirteen subjects (N=13) were studied for the low beta (13-21Hz) and high beta (22-30Hz) sub-bands, in four time-processing windows: 125ms, 250ms, 500ms, and 1s. In addition to obtaining the traditional Hurst estimator, the behavior of two derivatives of the Hurst exponent: the moving-Hurst (muHurst), and the mHurst, an estimator interpreted as a global entropic trend of the time series, were also evaluated. The results showed a clear differentiation between the entropic profiles obtained for low beta and high beta, for all individuals, as well as a different entropic structure of both sub-bands, when comparing Hurst exponents and their derivatives with different time processing windows. At the 1s scale (128 EEG frames), the average entropic content of the time series (muHurst) is relatively higher than the entropic load contained in the mid-term estimation of the whole signal (H(trad)). At the same time, estimates of mHurst, a derivative that assesses the overall entropic trend of the signal, indicated mostly lower entropic (more organized) content than estimates at shorter timescales. Our nonlinear exploration of the entropic structure of the brain revealed a wealth of details that can be used to complement the traditional characterization of the brain that linear approaches have provided so far. Our procedure allowed us to discern substantive differences between the entropic characteristics of the low beta and high beta EEG sub-bands. As well as individual differences within the group, potential substrates for the search of neural markers of psychological, pharmacological, or clinical utility.

Audio compression using transforms and high order entropy encoding

<span>Digital audio is required to transmit large sizes of audio information through the most common communication systems; in turn this leads to more challenges in both storage and archieving. In this paper, an efficient audio compressive scheme is proposed, it depends on combined transform coding scheme; it is consist of i) bi-orthogonal (tab 9/7) wavelet transform to decompose the audio signal into low & multi high sub-bands, ii) then the produced sub-bands passed through DCT to de-correlate the signal, iii) the product of the combined transform stage is passed through progressive hierarchical quantization, then traditional run-length encoding (RLE), iv) and finally LZW coding to generate the output mate bitstream. The measures Peak signal-to-noise ratio (PSNR) and compression ratio (CR) were used to conduct a comparative analysis for the performance of the whole system. Many audio test samples were utilized to test the performance behavior; the used samples have various sizes and vary in features. The simulation results appear the efficiency of these combined transforms when using LZW within the domain of data compression. The compression results are encouraging and show a remarkable reduction in audio file size with good fidelity.</span>

Design and Implementation of Robust Digital Watermarking using Hybrid technique for Copyright Protection Digital data

In this research paper, we concerned with the creation of a comprehensive digital watermarking framework based on DWT. In order to improve imperceptibility and robustness, the watermark is inserted only in chosen frames. The picked frames are the frames in which a change of scene happens. The key objective, therefore, is to detect the correct transformation of the scene. The Scene Shift Detector identifies correct frames that have been modified using the successive histogram discrepancy process. Two schemes proposed using the same method of scene detection. Both suggested schemes achieve a good watermark rating with good (PSNR) valuesThere, Because the watermark integration is done exclusively on the scene with low and high frequency DWT subbands, the image processing assaults, geometric aggressions, JPEG compression, high normalised image attacks are immune,and low-bit error rates (BER). Comparative analysis of two algorithms is also carried out.

SVM CLASSIFIER FOR EPILEPTIC SEIZURE PREDICTION USING SUB-BANDING OF IEEG SIGNALS

Epileptic seizure in the brain affects the day-to-day life of any individual due to its unexpected nature of occurrence. It has affected more than 50 million people worldwide. Drug resistance of patients is an important factor which leads to failure of epilepsy treatments using medications in 30% of patients. Surgery is also not a viable option in a substantial number of patients. In such cases, a new kind of seizure forecasting system is necessary to help those people. In our work, various sub-frequency bands of EEG signals are produced from the originally recorded Intracranial Electroencephalogram (IEEG) signals of five canines and two persons to identify possible low complex and less intense EEG features from each sub-band of the entire spectrum. Support Vector Machine (SVM) with different Kernel-based classifiers are used to categorize features into preictal and interictal data. Epileptic Seizures forecasting accuracy of 99% has been achieved for data from canine and human. Employed wavelet filter for noise removal and found that it improved the seizure prediction accuracy in some subjects and reduced the accuracy in some subjects. Similarly, the feature selection technique also improved the preictal detection accuracy in some patients/subjects and reduced the accuracy in some data. From this work, we identified that seizure prediction is possible in at least one sub-frequency band especially in high gamma sub-band generated from the originally recorded signal using a high-pass filter. This work demonstrates an algorithm for seizure forecasting or identifying the preictal region which identifies the suitable best sub-frequency band for predicting the seizure of the originally recorded EEG data by using the computationally less intense EEG features and employing the best classifying SVM kernel.

Image Retrieval using the Improved Double Density Contourlet Transform

A new image retrieval algorithm based on the improved double density contourlet transform(DDCT) is proposed in this paper. The improved double density contourlet transform have the property of shift invariance and non-Gaussian for the high frequency sub-bands. In order to obtain the high frequency texture feature, we design the quantization histograms using the high frequency direction sub-bands. The 0/1 quantization is used to deal with the energy matrix of high frequency sub-bands. A texture point is defined as an 8-D vector by integrating different channel values from high frequency sub-bands and the quantization histograms are computed. In order to get the texture-spatial features, the local bina ry pattern is used to describe the texture feature of low freq uency sub-band. Then the quantization histograms and the local binary pattern (LBP) can be used to denote the texture features of the image. Experiments show that the proposed algorithm using the improved double density contourlet transform outperforms the SD algorithm based on the contourlet transform in the natural image retrieval.

Three Stream Network Model for Lung Cancer Classification in the CT Images

Abstract Lung cancer is considered to be one of the deadly diseases that threaten the survival of human beings. It is a challenging task to identify lung cancer in its early stage from the medical images because of the ambiguity in the lung regions. This paper proposes a new architecture to detect lung cancer obtained from the CT images. The proposed architecture has a three-stream network to extract the manual and automated features from the images. Among these three streams, automated feature extraction as well as the classification is done using residual deep neural network and custom deep neural network. Whereas the manual features are the handcrafted features obtained using high and low-frequency sub-bands in the frequency domain that are classified using a Support Vector Machine Classifier. This makes the architecture robust enough to capture all the important features required to classify lung cancer from the input image. Hence, there is no chance of missing feature information. Finally, all the obtained prediction scores are combined by weighted based fusion. The experimental results show 98.2% classification accuracy which is relatively higher in comparison to other existing methods.

On Microstructure Estimation Using Flatbed Scanners for Paper Surface-Based Authentication

Paper surfaces under the microscopic view are observed to be formed by intertwisted wood fibers. Such structures of paper surfaces are unique from one location to another and are almost impossible to duplicate. Previous work used microscopic surface normals to characterize such intrinsic structures as a “fingerprint” of paper for security and forensic applications. In this work, we examine several key research questions of feature extraction in both scientific and engineering aspects to facilitate the deployment of paper surface-based authentication when flatbed scanners are used as the acquisition device. We analytically show that, under the unique optical setup of flatbed scanners, the specular reflection does not play a role in norm map estimation. We verify, using a larger dataset than prior work, that the scanner-acquired norm maps, although blurred, are consistent with those measured by confocal microscopes. We confirm that, when choosing an authentication feature, high spatial-frequency subbands of the heightmap are more powerful than the norm map. Finally, we show that it is possible to empirically calculate the physical dimensions of the paper patch needed to achieve a certain authentication performance in equal error rate (EER). We analytically show that log(EER) is decreasing linearly in the edge length of a paper patch.

A passive approach for the detection of splicing forgery in digital images

With the technology progress, a plethora of freely accessible software has questioned the authenticity of digital images. This field is continuously creating challenges for researchers to ascertain the integrity of images. Hence, there is a need to improve the performance of forgery detection algorithms from time to time. This paper is focused on the detection of splicing forgery because it is one of the most frequently used image manipulation techniques. In the proposed scheme, Markov features in both Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) domains are extracted and combined for the detection of image splicing. Three-level DWT is applied to the source image by the means of discrete Haar wavelet. The image is split in to high and low-frequency sub-bands after applying one level DWT. Furthermore, low-frequency sub-band is decomposed twice to obtain three-level DWT, which leads to more information and less amount of noise. The efficacy of the proposed scheme has been appraised on six benchmark datasets i.e. CASIA v2.0, DVMM, IFS-TC, CASIA v1.0, Columbia, and DSO-1. Moreover, the SVM classifier is trained to classify the images as tampered or authentic. The effectiveness of the proposed scheme is evaluated based on various performance parameters such as accuracy, sensitivity, specificity, and informedness. The proposed results show improved accuracy i.e. 99.69%, 99.76%, 97.80%, 98.61%, 96.90%, and 92.50% on CASIA v1.0, CASIA v2.0, DVMM, Columbia, IFS-TC, and DSO-1, respectively, in comparison to other existing approaches.

An Infrared and Visible Image Fusion Algorithm Based on LSWT-NSST

Regarding the problems of image distortion, edge blurring, Gibbs phenomena in the traditional wavelet transform algorithm and the loss of subtle features in the Non-Subsampled Shearlet Transform (NSST), and considering the physical characteristics of infrared and visible images, an infrared and visible image fusion algorithm based on the Lifting Stationary Wavelet Transform (LSWT) and Non-Subsampled Shearlet Transform is proposed in this paper. First, since LSWT can quickly calculate and has all advantages of traditional WT, it is utilized to decompose infrared and visible images to obtain low-frequency coefficients and multi-scale and multi-directional high-frequency coefficients, respectively. Second, NSST multi-scale decomposition is used to extract the target features and detailed features of the image from the high and low-frequency sub-bands to obtain new high and low-frequency sub-bands. Third, according to the physical characteristics that low and high-frequency coefficients represent, different fusion rules are designed. Discrete Cosine Transform (DCT) and Local Spatial Frequency (LSF) are introduced in the low-frequency sub-band, and LSF adaptive weighted fusion rules are used in the DCT domain. The fusion strategy improves the regional contrast in the high-frequency sub-band with the spectral characteristics of human vision. Finally, the Inverse Lifting Stationary Wavelet Transform (ILSWT) is used to reconstruct the fusion coefficients to obtain the final fused images. To verify the advantages of the proposed algorithm in this paper, the classic and advanced 9 IR and VI fusion algorithms are selected for subjective and objective comparison. In the objective evaluation, a comprehensive ranking index is designed based on 9 classical indicators. Simulation experiments with 10 IR and VI fusion algorithms prove that the proposed algorithm has better performance and flexibility. The results show that the proposed algorithm in this paper fuses the images with clear edges, prominent targets, and good visual perception, and it outperforms state-of-the-art image fusion algorithms.

Denoising of Uncertain Type Noise Images by Spatial Feature Classification in Nonsubsampled Shearlet Transform

Most denoising methods are designed to deal standard images with specific type noise, which do not perform well when denoising real noisy images contain uncertain types of noise. However, underwater image is a typical uncertain type noise image. To solve this problem, this paper presents a method using spatial feature classification jointing nonsubsampled shearlet transform (NSST) for denoising uncertain type noise images. Justifiable granule is employed to solve the problem of parameter selection. The raw image was decomposed by using the NSST to get one low frequency subband and several high frequency subbands. Then, the preliminary binary map is built, the binary map is employed to decide whether a coefficient contains spatial feature or not. And we employ justifiable granule to solve the difficulty of parameter selection. The high subbands coefficients are classified into two classes by fuzzy support vector machine classification: the texture class and the noise class. At last, the adaptive Bayesian threshold is used to shrink the coefficients. Simulation results show the proposed method is effective in uncertain type noise images(also have good performance in specific type noise). The method we proposed has been compared with other popular denoising methods and get excellent subjective performance and PSNR improvement.

K-complexes Detection in EEG Signals using Fractal and Frequency Features Coupled with an Ensemble Classification Model

K-complexes are important transient bio-signal waveforms in sleep stage 2. Detecting k-complexes visually requires a highly qualified expert. In this study, an efficient method for detecting k-complexes from electroencephalogram (EEG) signals based on fractal and frequency features coupled with an ensemble model of three classifiers is presented. EEG signals are first partitioned into segments, using a sliding window technique. Then, each EEG segment is decomposed using a dual-tree complex wavelet transform (DT-CWT) to a set of real and imaginary parts. A total of 10 sub-bands are used based on four levels of decomposition, and the high sub-bands are considered in this research for feature extraction. Fractal and frequency features based on DT-CWT and Higuchi’s algorithm are pulled out from each sub-band and then forwarded to an ensemble classifier to detect k-complexes. A twelve-feature set is finally used to detect the sleep EEG characteristics using the ensemble model. The ensemble model is designed using a combination of three classification techniques including a least square support vector machine (LS-SVM), k-means and Naïve Bayes. The proposed method for the detection of the k-complexes achieves an average accuracy rate of 97.3 %. The results from the ensemble classifier were compared with those by individual classifiers. Comparisons were also made with existing k-complexes detection approaches for which the same datasets were used. The results demonstrate that the proposed approach is efficient in identifying the k-complexes in EEG signals; it yields optimal results with a window size 0.5 s. It can be an effective tool for sleep stages classification and can be useful for doctors and neurologists for diagnosing sleep disorders.

Comparative Analysis of Multi-scale Wavelet Decomposition and k-Means Clustering Based Text Extraction

Text present in images provides important information for automatic annotation, indexing and retrieval. Therefore, its extraction is a well-known research area in computer vision. However, variations of text due to differences in orientation, alignment, font, size, contrast and complex background makes the problem of text extraction extremely challenging. In this paper, we propose an efficient method to extract text regions even under complex background using DWT and k-means clustering along with voting decision process. As textures of text have abrupt variation and irregular texture property in the wavelet transform domain, so wavelet transform seems to be the best choice for achieving the objective of image segmentation. A small size overlapping sliding window is used to scan high frequency component sub-bands from which texture features are extracted. On the basis of these features, k-means clustering is employed to classify the image into text and background clusters. Finally, voting decision process and area-based filtering are used to locate text regions accurately. We examined and evaluated the performance by varying wavelet functions and decomposition levels. The proposed method is evaluated on four standard datasets (ICDAR 2013, KAIST, MSRA-TD500, SVT) and own created dataset. Further, performance analysis reveals that this method is robust and efficient for extracting text regions under various conditions.

High-Quality Wavelets Features Extraction for Handwritten Arabic Numerals Recognition

Arabic handwritten digit recognition is the science of recognition and classification of handwritten Arabic digits. It has been a subject of research for many years with rich literature available on the subject. Handwritten digits written by different people are not of the same size, thickness, style, position or orientation. Hence, many different challenges have to overcome for resolving the problem of handwritten digit recognition. The variation in the digits is due to the writing styles of different people which can differ significantly. Automatic handwritten digit recognition has wide application such as automatic processing of bank cheques, postal addresses, and tax forms. A typical handwritten digit recognition application consists of three main stages namely features extraction, features selection, and classification. One of the most important problems is feature extraction. In this paper, a novel feature extraction approach for off-line handwritten digit recognition is presented. Wavelets-based analysis of image data is carried out for feature extraction, and then classification is performed using various classifiers. To further reduce the size of training data-set, high entropy subbands are selected. To increase the recognition rate, individual subbands providing high classification accuracies are selected from the over-complete tree. The features extracted are also normalized to standardize the range of independent variables before providing them to the classifier. Classification is carried out using k-NN and SVMs. The results show that the quality of extracted features is high as almost equivalently high classification accuracies are acquired for both classifiers, i.e. k-NNs and SVMs.

Power-aware VLSI design of reversible watermarking for access control

A lifting based reversible data hiding is introduced here. The low–high subband is subdivided into little blocks of size (4 × 4), to generate a content dependent watermark. Then the access management is done by permutation of the content dependent watermark by a user-specific covert key. The permuted watermark is employed to modulate the lifting coefficients of the low–high subband. The modulation causes degradation of the visual quality of the host image. That plays an important role in access management through inverse method. Lastly, a low-power ‘very-large-scale-integration’ architectural hardware of this scheme is designed and synthesized on a ‘field programmable gate array’. The experiment is conducted over a variety of benchmark images and the results establish the superiority of the method. It is also observed that in real-time processing, the scheme consumes 63.26% less power than the related implementation found in the literature, for watermarking encoder and decoder at a maximum operating frequency of 130.186 MHz for the processing of (512 × 512) sized images.

Performance comparison of recent optimization algorithm Jaya with particle swarm optimization for digital image watermarking in complex wavelet domain

Nowadays copyright protection is mandatory in the field of image processing to removes the illegitimate utilization and imitation of digital images. The digital image watermarking is one of the most reliable methods for protecting the illegal validation of data. In this paper, singular value decomposition based digital image watermarking scheme is proposed in complex wavelet transform (CWT) domain using intelligence algorithms like particle swarm optimization (PSO) and recently proposed Jaya algorithm. The watermark image is embedded into high frequency CWT subband of cover image. At the time of watermark embedding and extraction, optimization algorithms Jaya and PSO are applied to improve the robustness and imperceptibility by assessing the fitness function. The perceptual quality of watermarked image and robustness of extracted watermark image are verified under the filtering, rotation, scaling, Gaussian noise and JPEG compression attacks. From the comparative analysis it is proved that Jaya algorithm is better as compared to PSO algorithm under most types of attacks with higher magnitudes whereas identical under the lower magnitude of applied attacks. Moreover, using variety of cover images, it is found that, the elapse time and value of fitness function given by Jaya algorithm are also better as compared to PSO.

Person Recognition by Hilbert Pair of Wavelets using Facial Images

In human identification, the face acts as an important tool that carries the identity of each person. The human mind has the ability to re-cognize faces after the first view of a human face. Though there are many types of face detection/recognition system found no method can give the 100% accurate outputs. In this proposed system we are implementing and analyzing a new method that can be used for person recognition system that can produce better output accuracies. In the proposed system of person recognition method, one of the robust wavelet transform methods is used for the extraction of the features from the original images. The wavelet type used is known as the Dual Tree M-Band Wavelet Transform (DTMBWT) method. Using this transform the low and high sub-bands is obtained. These low and high sub-band coefficients are given as the input for the classification purpose. The sub-band obtained from the DTMBWT transform is given as the inputs for the classification purpose. The classification process is done using the K-Nearest Neighbor (KNN) classifier scheme. The system is implemented by using the facial images from the ORL database. By using this dataset images the performance measures of the proposed system is calculated in the form of graphical results such as Receiver Operating Characteristic (ROC), Inverse ROC and Expected Performance Curve (EPC) curves. Results show that proposed DTMBWT based face recognition provides better results than other approaches.

A multiresolution processing method for contrast enhancement in portal imaging

Portal images have a unique feature among the imaging modalities used in radiotherapy: they provide direct visualization of the irradiated volumes. However, contrast and spatial resolution are strongly limited due to the high energy of the radiation sources. Because of this, imaging modalities using x-ray energy beams have gained importance in the verification of patient positioning, replacing portal imaging.The purpose of this work was to develop a method for the enhancement of local contrast in portal images. The method operates in the sub-bands of a wavelet decomposition of the image, re-scaling them in such a way that coefficients in the high and medium resolution sub-bands are amplified—an approach totally different from those operating on the image histogram, widely used nowadays.Portal images of an anthropomorphic phantom were acquired in an electronic portal imaging device. Then, different re-scaling strategies were investigated, studying the effects of the scaling parameters on the enhanced images. Also, the effect of using different types of transform was studied. Finally, the implemented methods were combined with histogram equalization methods like the contrast limited adaptive histogram equalization, and these combinations were compared.Uniform amplification of the detail sub-bands shows the best results in contrast enhancement. On the other hand, linear re-escalation of the high resolution sub-bands increases the visibility of fine detail in the images, at the expense of an increase in noise levels. Also, since processing is applied only to detail sub-bands, not to the approximation, the mean gray level of the image is minimally modified and no further display adjustments are required.It is shown that re-escalation of the detail sub-bands of portal images can be used as an efficient method for the enhancement of both the local contrast and the resolution of these images.