Stationary Wavelet Transform Research Articles

Multimodal medical image fusion based on Yager’s intuitionistic fuzzy sets

The objective of image fusion for medical images is to combine multiple images obtained from various sources into a single image suitable for better diagnosis. Most of the state-of-the-art image fusing technique is based on nonfuzzy sets, and the fused image so obtained lags with complementary information. Intuitionistic fuzzy sets (IFS) are determined to be more suitable for civilian, and medical image processing as more uncertainties are considered compared with fuzzy set theory. In this paper, an algorithm for effectively fusing multimodal medical images is presented. In the proposed method, images are initially converted into Yager’s intuitionistic fuzzy complement images (YIFCIs), and a new objective function called intuitionistic fuzzy entropy (IFE) is employed to obtain the optimum value of the parameter in membership and non-membership functions. Next, the YIFCIs are compared using contrast visibility (CV) to construct a decision map (DM). DM is refined with consistency verification to create a fused image. Simulations on several pairs of multimodal medical images are performed and compared with the existing fusion methods, such as simple average, discrete cosine transform (DCT), redundant wavelet transform (RWT), intuitionistic fuzzy set, fuzzy transform and interval-valued intuitionistic fuzzy set (IVIFS). The superiority of the proposed method is presented and is justified. Fused image quality is also verified with various quality metrics, such as spatial frequency (SF), average gradient (AG), fusion symmetry (FS), edge information preservation (QAB/F), entropy (E) and computation time (CoT).

MRI denoising by NeighShrink based on chi-square unbiased risk estimation.

NeighShrink is an efficient image denoising algorithm for the reduction of additive white Gaussian noise. However, it does not perform well in terms of Rician noise removal for MRI (Magnetic Resonance Imaging). Allowing for the characteristics of squared-magnitude MR (Magnetic Resonance) images, which follow a non-central chi-square distribution, the CURE (Chi-Square Unbiased Risk Estimation) is used to determine an optimal threshold for NeighShrink. Therefore, we propose the NeighShrinkCURE denoising algorithm. Bilateral filtering and cycle spinning are used to further improve denoising performance. Experimental results show that the proposed algorithm is simple and efficient, and provides good noise reduction while preserving edges and details well. Compared with some similar MRI denoising algorithms, the proposed algorithm has improvements in PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). Although running time of the proposed algorithm has some increment compared with some current MRI denoising algorithms, the comprehensive performance of the proposed algorithm is superior to several current MRI denoising algorithms.

Multiscale fusion of multimodal medical images using lifting scheme based biorthogonal wavelet transform

Medical image fusion has been used to improve useful and relevant information (e.g., precise localization of abnormalities) of multimodal medical images, such as computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) images, and the information obtained from the fused images can then be utilized as an assistive tool for better diagnosis and treatment. In this work, we propose an algorithm for fusing multimodal medical images using lifting scheme-based biorthogonal wavelet transform. The multiscale fusion scheme is performed for the multimodal medical images in the wavelet domain at multiple scales by adopting either average or absolute maximum fusion rules. To verify the effectiveness of the proposed method, a series of visual and quantitative performance evaluations of the proposed method are compared with those of five representative wavelet-based fusion methods including contourlet transform (CLT), nonsubsampled CLT (NSCLT), lifting wavelet transform (LWT), multiwavelet transform (MWT), and stationary wavelet transform (SWT). For the quantitative performance evaluations, we adopted five metrics: fusion factor, fusion symmetry, entropy, standard deviation and edge strength. The experimental results demonstrated that the proposed method could yield better results than other wavelet transform-based fusion methods. Furthermore, from the additional comparison study of fusing noise-contaminated images, we could conclude that the proposed method is noise resilient in fusing images corrupted by Gaussian and speckle noise with varying variances.

Multifocus image fusion scheme based on discrete cosine transform and spatial frequency

Multifocus images are different images of the same scene captured with different focus in the cameras. These images when considered individually may not give good quality. Hence to obtain a good quality image, this work proposes an algorithm for fusing multifocus images using Discrete Cosine Transform and spatial frequency. The proposed algorithm works for fusing any number of images. The second step calculates the average and maximum of all the source images and reduces the source images to be processed as two. Then Discrete Cosine Transform (DCT) is applied over the two input images. Min-Max normalization is done on the DCT coefficients and fusion is done using spatial frequency. Inclusion of the second step of the proposed algorithm in some existing algorithms such as Stationary Wavelet Transform, Principal Component Analysis and spatial fusion improves the performance. The metrics used for evaluation proves that the proposed algorithm gives better results than the other algorithms using DCT and state of the art techniques.

Combination of wavelet transform and singular value decomposition-based contrast enhancement technique for target detection in UAV reconnaissance thermal images

ABSTRACTIn Aerial surveillance, thermal images acquired by unmanned aerial vehicle (UAV) are greatly affected due to various external interferences, which results in a low contrast image. Widely used conventional contrast enhancement methods such as histogram equalization and dynamic range partitioning techniques suffer from severe brightness changes and reduced sharpness, which in turn fail to preserve the edge details of the image. Thus for efficient target detection, it is essential to develop effective thermal infrared image contrast and edge enhancement technique. In this paper, wavelet transform (WT) and singular value decomposition (SVD)-based image enhancement technique is attempted for the target detection using thermal images captured by UAV. The discrete wavelet transform (DWT), stationary wavelet transform (SWT) and SVD are used for texture feature enhancement, edge enhancement and illumination correction, respectively. The experimental results show that the proposed technique yields higher entropy (6.7485), EMEE (2.1212), MSSIM (0.8719) and lower AMBE (21.9049) values when compared to other existing techniques.

Microwave Imaging of Nonsparse Object Using Dual-Mesh Method and Iterative Method With Adaptive Thresholding

A new microwave image reconstruction method is proposed to recover a nonsparse object using the iterative method with adaptive thresholding for compressed sensing (IMATCS) method. First, a dual-mesh method discretizes the object using the Distmesh generator tool, which automatically generates size functions and adapts to the curvature and the feature size of the geometry. Near field scattered data collected from a circular phantom are used to solve the inverse scattering problem using the distorted Born iterative method (DBIM). At every iteration during DBIM, the forward problem is formulated as an underdetermined set of linear equations. Then, the IMATCS method is applied to solve the linear equations. IMATCS is a $L_{2}$ -regularized approach to solve the sparse recovery problem, which improves the robustness of the algorithm. Next, the nondecimated wavelet transform (NDWT) method is applied to transform the nonsparse signal into a sparse signal which acts as an input to the IMATCS process. As a result, the new proposed NDWT based on IMATCS method (NDW-IMATCS) is able to recover the nonsparse signal and leads to the reconstruction results. Finally, we demonstrate that our dual-mesh method combined with the NDW-IMATCS algorithm leads to fast, accurate, and stable reconstructions of nonsparse objects.

Satellite Image Enhancement using Wavelet-domain based on Singular Value Decomposition

Improving the quality of satellite images has been considered an essential field of research in remote sensing and computer vision. There are currently numerous techniques and algorithms used to achieve enhanced performance. Different algorithms have been proposed to enhance the quality of satellite images. However, satellite images enhancement is considered a challenging task and may play an integral role in a wide range of applications. Having received significant attention in recent years, this manuscript proposes a methodology to enhance the resolution and contrast of satellite images. To improve the quality of satellite images, in this study, first, the resolution of an image is improved. For resolution enhancement, first, the input image is decomposed into four frequency components (LL,LH,HL,and HH) using the stationary wavelet transform (SWT). Second, Singular value matrices (SVMs) U_A and V_A which contains high-frequency elements of an input image are obtained using singular value decomposition (SVD). Third, the high-frequency components (LH,HL) of an input image are obtained using discrete wavelet transform (DWT) and corrected by SVMs and SWT. Next, the interpolation factor is added and the high-resolution image is obtained using inverse discrete wavelet transform (IDWT). Second, the contrast of the image is optimized. For the contrast enhancement, the image is decomposed using DWT into sub-bands such as (LL,LH,HL,and HH). Next, the singular value matrix (SVM) of the LL sub-band is obtained which contains the illumination information. Then, SVM is modified to enhance the contrast. Finally, the image reconstructed using the IDWT. In this paper, the results from the method above are compared with existing approaches. The proposed method achieves high performance and yields more insightful results over conventional technique.

Multi -Focus Image Fusion Based on Stationary Wavelet Transform and PCA on YCBCR Color Space

The multi-focus image fusion method can fuse more than one focused image to generate a single image with more accurate description. The purpose of image fusion is to generate one image by combining information from many source images of the same scene. In this paper, a multi-focus image fusion method is proposed with a hybrid pixel level obtained in the spatial and transform domains. The proposed method is implemented on multi-focus source images in YCbCr color space. As the first step two-level stationary wavelet transform was applied on the Y channel of two source images. The fused Y channel is implemented by using many fusion rule techniques. The Cb and Cr channels of the source images are fused using principal component analysis (PCA). The proposed method performance is evaluated in terms of PSNR, RMSE and SSIM. The results show that the fusion quality of the proposed algorithm is better than obtained by several other fusion methods, including SWT, PCA with RGB source images and PCA with YCbCr source images.

A Hybrid Approach to Gender Classification using Speech Signal

Speech forms a significant means of communication and the variation in pitch of a speech signal of a gender is commonly used to classify gender as male or female. In this study, we propose a system for gender classification from speech by combining hybrid model of 1-D Stationary Wavelet Transform (SWT) and artificial neural network. Features such as power spectral density, frequency, and amplitude of human voice samples were used to classify the gender. We use Daubechies wavelet transform at different levels for decomposition and reconstruction of the signal. The reconstructed signal is fed to artificial neural network using feed forward network for classification of gender. This study uses 400 voice samples of both the genders from Michigan University database which has been sampled at 16000 Hz. The experimental results show that the proposed method has more than 94% classification efficiency for both training and testing datasets.

A Hybrid LSTM Neural Network for Energy Consumption Forecasting of Individual Households

Irregular human behaviors and univariate datasets remain as two main obstacles of data-driven energy consumption predictions for individual households. In this study, a hybrid deep learning model is proposed combining an ensemble long short term memory (LSTM) neural network with the stationary wavelet transform (SWT) technique. The SWT alleviates the volatility and increases the data dimensions, which potentially help improve the LSTM forecasting accuracy. Moreover, the ensemble LSTM neural network further enhances the forecasting performance of the proposed method. Verification experiments were performed based on a real-world household energy consumption dataset collected by the ‘UK-DALE project. The results show that, with a competitive training efficiency, the proposed method outperforms all compared state-of-art methods, including the persistent method, support vector regression (SVR), long short term memory (LSTM) neural network and convolutional neural network combining long short term memory (CNN-LSTM), with different step sizes at 5, 10, 20 and 30 minutes, using three error metrics.

Methylcyclohexane Continuous Distillation Column Fault Detection Using Stationary Wavelet Transform & Fuzzy C-means

It is extremely difficult to develop fault detection and diagnosis approaches for systems composed of several complex processes. This work is placed in the context of detection and diagnosis of the operating faults of an industrial installation. Indeed, the installation in this study is a Methylcyclohexane continuous column from a mixture of toluene/methylcyclohexane in which the mass composition was defined to 23% of methylcyclohexane. The studied system, allows the separation of the more volatile component which is methylcyclohexane contained in the liquid mixture. We use K-means clustering technique combined with wavelet transform for noise reduction in order to determine precisely data mapping to different classes of faults in the distillation column. This approach is compared with the exclusive use of K-means and its classification accuracy is proved. The developed technique is implemented and tested against a dataset, which covers two modes of operation of the distillation column: the normal mode, and the abnormal mode. The latter mode is represented by the four most common dysfunctions of the distillation column.

IMPLEMENTATION OF QIM BASED AUDIO WATERMARKING USING HYBRID TRANSFORM OF SWT-DCT-SVD METHODS OPTIMIZED WITH GENETIC ALORITHM

Nowadays, almost all data transaction is done through the internet because it is easy and could be accessed anywhere. The file is uploaded directly without any security or scanning making people able to upload illegal files or files that is not owned by them. This violation of copyright becomes a huge problem as it reduces the owner�s profit. That is why watermarking method is created. Watermarking is a method of embedding secret information to a host data. The information could be embedded into an audio, image or video data. This research will design an audio watermarking by combining 3 methods of transformation: Stationary Wavelet Transform (SWT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD). SWT separates data�s frequency into high and low. After that, DCT maps correlated high frequency data into uncorrelated coefficient. Then those coefficients will be deconstructed into three matrices u, s, and v using SVD method. Later, the s matric will be embedded with the watermark. With these methods, watermarked audio produce SNR>20dB and BER�0,1170 in average.

Wavelet Cross-correlation in Bivariate Time-Series Analysis

The estimation of the correlation between independent data sets using classical estimators, such as the Pearson coefficient, is well established in the literature. However, such estimators are inadequate for analyzing the correlation among dependent data. There are several types of dependence, the most common being the serial (temporal) and spatial dependence, which are inherent to the data sets from several fields. Using a bivariate time-series analysis, the relation between two series can be assessed. Further, as one time series may be related to an other with a time offset (either to the past or to the future), it is essential to also consider lagged correlations. The cross-correlation function (CCF), which assumes that each series has a normal distribution and is not autocorrelated, is used frequently. However, even when a time series is normally distributed, the autocorrelation is still inherent to one or both time series, compromising the estimates obtained using the CCF and their interpretations. To address this issue, analysis using the wavelet cross-correlation (WCC) has been proposed. WCC is based on the non-decimated wavelet transform (NDWT), which is translation invariant and decomposes dependent data into multiple scales, each representing the behavior of a different frequency band. To demonstrate the applicability of this method, we analyze simulated and real time series from different stochastic processes. The results demonstrated that analyses based on the CCF can be misleading; however, WCC can be used to correctly identify the correlation on each scale. Furthermore, the confidence interval (CI) for the results of the WCC analysis was estimated to determine the statistical significance.

CORE-PI: Non-iterative convolution-based reconstruction for parallel MRI in the wavelet domain.

To develop and test a novel parameter-free non-iterative wavelet domain method for reconstruction of undersampled multicoil MR data. A linear parallel MRI method that operates in the Stationary Wavelet Transform (SWT) domain is proposed. The method is coined COnvolution-based REconstruction for Parallel MRI (CORE-PI). This method computes the SWT of the unknown MR image directly from subsampled k-space measurements, without modifying the RF excitation pulse. It then reconstructs the image using the wavelet filter bank approach, with simple linear computations. The CORE-PI implementation is demonstrated by experiments with a numeric brain phantom and invivo brain scans data, with various wavelet types and high reduction factors. It is compared to the well-known parallel MRI methods GRAPPA and l1-SPIRiT. The experimental results show that CORE-PI is suitable for different 1D Cartesian k-space undersampling schemes, including regular and irregular ones, and for wavelets of different families. CORE-PI accurately reconstructs the SWT coefficients of the unknown MR image; this wavelet-domain decomposition is fully computed despite the k-space undersampling. Furthermore, CORE-PI provides high-quality final reconstructions, with an average NRMSE of 0.013, which is significantly lower than that obtained by GRAPPA and l1-SPIRiT. Moreover, CORE-PI offers significantly faster computation times: the typical CORE-PI runtime is about 60seconds, which is about 20% shorter than that of l1-SPIRiT and 55%-75% shorter than that of GRAPPA. COnvolution-based REconstruction for Parallel MRI advantageously offers: (a) flexible 1D undersampling of a Cartesian k-space, (b) a parameter-free non-iterative implementation, (c) reconstruction performance comparable or better than that of GRAPPA and l1-SPIRiT, and (d) robust fast computations.

Internet of image things-discrete wavelet transform and Gabor wavelet transform based image enhancement resolution technique for IoT satellite applications

In image processing, image enhancement is a vital processing chore. The image enhancement can improve the image quality by removing either blur or any kind of noise in the image. Image enhancement technique is utilized in many applications, such as medical, satellite, agriculture, oceanography and so on. This paper focuses on the IoT satellite applications. Most of the satellite images are essential to have high resolution satellite images, low resolution images are majorly affected by absorption, scattering, spatial resolution and spectral resolution issues. For better resolution of these kinds of issued images, Discrete Wavelet Transform (DWT) based interpolation method, combination of DWT and stationary wavelet transform (SWT) methods, bicubic interpolation methods are utilized. However, DWT with SWT method is failed avoid distorted in the resultant images, the bicubic interpolation method is quite complex and cannot give a clear image. DWT based interpolation method lose linear features and unwanted oscillations are occurred and edges data is lost. Therefore, DWT and Gabor technique is proposed to overcome existing method issues. DWT is decomposed into multiple sub-bands; GWT is employed to minimize the loss of information in wavelet domain. The advantages of the GWT are less complexity, remove the noise, and also sharp the image. The proposed method of the PSNR, MSE is compared with existing methods by using the different satellite images.

Video Steganography using 3D Stationary Wavelet Transform

Video steganography aims at hiding important data in video files. Nowadays, video steganography is drawing much more attention. That is because videos can carry a large amount of secret data. In addition, they are transmitted frequently on the internet websites such as Facebook and YouTube imposing a larger practical significance on video steganography. In this paper, the 3D Stationary Wavelet Transform (SWT) is used to hide a secret image within video frames. The frames that contain motion are detected using the 3D SWT, then the wavelet coefficients of the secret image are hidden in the 3D SWT sub-bands. The proposed system was tested using some gray and color secret images. Different video formats were used such as MPEG, avi, mov and wmv. The experimental results show that the proposed method successfully hides the secret image within the video file with a similarity near 100% and the Peak Signal-to-Noise Ratio (PSNR) value above 30. The proposed method achieved promising results both quantitatively and qualitatively.

Autonomic modulation and epileptic seizures: Biofeedback therapy for epilepsy

Long-term video-EEG monitoring has improved diagnosis and treatment of epilepsy, especially in children. However, the amount of data neurophysiologists must analyze has grown remarkably.The main purpose of this paper is to provide a diagnostic support to speed up and ease EEG interpretation for a specific application concerning absence seizures, a type of non-motor generalized epileptic seizures.The proposed method consists of a pre-processing step where signals are filtered through the Stationary Wavelet Transform for the reduction of possible artefacts. Subsequently, a supervised automatic classification method is implemented for seizure detection, based on the Support Vector Machine Fine Gaussian method. Finally, a post-processing step is implemented in which spatial and temporal thresholds are defined for both online and offline application.In addition, a method that applies sonification techniques is developed. Sonification techniques could speed up the process of interpreting information, allowing rapid clinical intervention and a continuous monitoring of the event.The dataset consists of 30 EEG recordings performed in 24 children with absence seizures, clinically evaluated at the Meyer Children's Hospital in Firenze, Italy.The method shows encouraging results both in terms of balanced accuracy (about 96%) and latency times (1.25 s on average), which might make it suitable for online clinical trials. In fact, it was implemented in the perspective of a possible real-time application in clinical practice.

EEG Based Strategies for Human Gustation Classification Using Spartan—6 FPGA

Gustation is one of the five sensations and is responsible for the inhibition of electrical stimulation, thus reflecting in the overall cortical activity of the brain due to the electrical stimulation. The information processing of gustation are employed for disorder recognition such as Parkinson’s disease, etc. and is a source of reimbursement for better clinical diagnosis. EEG signal of gustatory stimulus is preprocessed using IIR band pass filter to remove the artifacts due to noise. The advantage of using IIR filter is the requirement of lesser memory space and works faster than FIR filter. Extraction of features such as Short Time Fourier Transform and Fast Fourier Transform are frequency-domain analysis in which the signal is assumed to be stationary. An algorithm for feature extraction used in the proposed work is Stationary Wavelet Transform (SWT) which provides time frequency representation. In time domain, the statistical features of the detailed coefficients are computed and a filtered EEG signal is decomposed using SWT. The statistical features computed here are the mean which is an average of the absolute value of the EEG signal, variance which uses the power of the EEG signal as a feature of the signal and power spectral density. The different gustatory stimuli of bitter, sour and sweet are classified based on the features extracted. Lastly, the feature extraction and preprocessing algorithms are implemented in Spartan—6 FPGA kit. The proposed system possesses an advantage of increased computational speed and reduced area utilization compared to existing method.

Enhancement of IR images using histogram processing and the Undecimated additive wavelet transform

This paper presents a fabulous enhancement approach for infrared (IR) images. This approach mixes the benefits of the undecimated Additive Wavelet Transform (AWT) with the homomorphic transform and Contrast Limited Adaptive Histogram Equalization (CLAHE). The basic idea of this approach depends on applying the CLAHE on the IR image. Then, the resultant image is decomposed into sub-bands using the AWT. The homomorphic enhancement is implemented on each sub-band, separately, up to the sixth sub-band. The homomorphic enhancement is applied on the IR image in the log domain by decomposing the image into illumination and reflectance components. The illumination is attenuated, while the reflectance is magnified. Applying this method on each sub-band gives more details in the IR image. The performance quality metrics for the suggested approach are entropy, average gradient, contrast, and Sobel edge magnitude. Simulation results reveal the success of the proposed approach in enhancing the quality of IR images.

Fusion of Three Band Magnetic Resonance Images Using Sharp Frequency Localized Contourlet Transform

Background: T1, T2 and PD weighted images are the three bands of magnetic resonance image and they provide complementary information of the structure of the part being imaged. It is more useful for diagnosis and analysis if they are fused into MRI triplet. Discussion: This paper proposes a novel fusion method using Selective Frequency Localization Contourlet Transform to combine these three bands into one. Conclusion: From the qualitative and quantitative evaluation using average pixel intensity, standard deviation, mutual information and edge information based objective criteria, the fused image is found to be more informative. Keywords: Magnetic resonance images, cycle spinning, contourlet transform, spatial frequency, sum modified laplacian, fusion method.