Analysis of Discrete Wavelet Transforms Variants for the Fusion of CT and MRI Images

Background: Medical image fusion methods are applied to a wide assortment of medical fields, for example, computer-assisted diagnosis, telemedicine, radiation treatment, preoperative planning, and so forth. Computed Tomography (CT) is utilized to scan the bone structure, while Magnetic Resonance Imaging (MRI) is utilized to examine the soft tissues of the cerebrum. The fusion of the images obtained from the two modalities helps radiologists diagnose the abnormalities in the brain and localize the position of the abnormality concerning the bone. Methods: Multimodal medical image fusion procedure contributes to the decrease of information vulnerability and improves the clinical diagnosis exactness. The motive is to protect salient features from multiple source images to produce an upgraded fused image. The CT-MRI image fusion study made it conceivable to analyze the two modalities straightforwardly. Several states of the art techniques are available for the fusion of CT & MRI images. The discrete wavelet transform (DWT) is one of the widely used transformation techniques for the fusion of images. However, the efficacy of utilization of the variants of wavelet filters for the decomposition of the images, which may improve the image fusion quality, has not been studied in detail. Therefore the objective of this study is to assess the utility of wavelet families for the fusion of CT and MRI images. In this paper investigation on the efficacy of 8 wavelet families (120 family members) on the visual quality of the fused CT & MRI image has been performed. Further, to strengthen the quality of the fused image, two quantitative performance evaluation parameters, namely classical and gradient information, have been calculated. Results: Experimental results demonstrate that amongst the 120 wavelet family members (8 wavelet families), db1, rbio1.1, and Haar wavelets have outperformed other wavelet family members in both qualitative and quantitative analysis. Conclusion: Quantitative and qualitative analysis shows that the fused image may help radiologists diagnose the abnormalities in the brain and localize the position of the abnormality concerning the bone more easily. For further improvement in the fused results, methods based on deep learning may be tested in the future.

Investigating an Efficient and Accurate Protocol for Sampling Structures from Molecular Dynamics Simulations: A Close Look by Different Wavelet Families

Molecular Dynamics (MD) simulations are widely used to predict the behavior of molecular systems over time. However, one of the great challenges of MD simulations is how to treat the thousands of configurations obtained from calculations, since the number of the quantum calculations (QM) required for evaluating electronic parameters is too high and, sometimes, computationally impracticable. Thus, an efficient and accurate sampling protocol is essential for combining classical MD and QM calculations. In this article, based on the OWSCA methodology, 93 wavelet signals were analyzed in order to further refine the methodology and identify the best wavelet family for [Fe(H2O)6]2+ and [Mn(H2O)6]2+ complexes in solution. Our results point out that the bior1.3 was the best wavelet, values closest to the experimental data were obtained for both studied systems.

Performance of reconstruction factors for a class of new complex continuous wavelets

In this paper, we determine the factors necessary for the reconstruction of the signal from its continuous wavelet transform performed using the new complex continuous wavelet family by making use of admissible conditions and studied some of its properties. We also make a comparative assessment of its performance with the existing complex continuous wavelets, such as Morlet, Paul and DOG, in terms of reconstruction capability. The reconstruction was performed on three data sets, namely, a signal with a mixture of low and high frequencies, a non-stationary signal (synthetic) and an ECG signal. The results show that the proposed family of wavelets reconstruction capability is comparable with Morlet, Paul and DOG wavelets. Further, we investigate an alternate reconstruction formula without making use of admissibility condition and compare its efficiency of reconstruction with the standard (restricted) Morlet wavelet.

General model for best feature extraction of EEG using discrete wavelet transform wavelet family and differential evolution

Wavelet family and differential evolution are proposed for categorization of epilepsy cases based on electroencephalogram (EEG) signals. Discrete wavelet transform is widely used in feature extraction step because it efficiently works in this field, as confirmed by the results of previous studies. The feature selection step is used to minimize dimensionality by excluding irrelevant features. This step is conducted using differential evolution. This article presents an efficient model for EEG classification by considering feature extraction and selection. Seven different types of common wavelets were tested in our research work. These are Discrete Meyer (dmey), Reverse biorthogonal (rbio), Biorthogonal (bior), Daubechies (db), Symlets (sym), Coiflets (coif), and Haar (Haar). Several kinds of discrete wavelet transform are used to produce a wide variety of features. Afterwards, we use differential evolution to choose appropriate features that will achieve the best performance of signal classification. For classification step, we have used Bonn databases to build the classifiers and test their performance. The results prove the effectiveness of the proposed model.

Haar wavelet collocation method for linear first order stiff differential equations

In general, there are countless types of problems encountered from different disciplines that can be represented by differential equations. These problems can be solved analytically in simpler cases; however, computational procedures are required for more complicated cases. Right at this point, the wavelet-based methods have been using to compute these kinds of equations in a more effective way. The Haar Wavelet is one of the appropriate methods that belongs to the wavelet family using to solve stiff ordinary differential equations (ODEs). In this study, The Haar Wavelet method is applied to stiff differential problems in order to demonstrate the accuracy and efficacy of this method by comparing the exact solutions. In comparison, similar to the exact solutions, the Haar wavelet method gives adequate results to stiff differential problems.

Analysis on Wavelet Family based Channel Estimation for MIMO-OFDM System

Channel estimation for (MIMO-OFDM) is an important part for present and future generation broadband wireless communications. OFDM, which uses for the spaced subcarriers to improving the performance. The channel estimation schemes based on pilot reduces the transmission rate and spectral efficiency. Many conventional schemes of channel estimation are not effective in reducing noise. It leads to poor quality signals at receiver at final stage. To rectify this problem, in this paper a wavelet family based channel estimation technique, is proposed and analyzed. The proposed research aims to estimate channels and reconstruct signal via wavelet transform, dyadic wavelet transform and fractional spline wavelet transform which enhances the spectrum efficiency and transmission rate. Simulation results shows that the fractional spline wavelet transform performs well for channel estimation and data signal reconstruction.

A wavelet-based method for MRI liver image denoising

Image denoising stays be a standout amongst the primary issues in the field of image processing. Several image denoising algorithms utilizing wavelet transforms have been presented. This paper deals with the use of wavelet transform for magnetic resonance imaging (MRI) liver image denoising using selected wavelet families and thresholding methods with appropriate decomposition levels. Denoised MRI liver images are compared with the original images to conclude the most suitable parameters (wavelet family, level of decomposition and thresholding type) for the denoising process. The performance of our algorithm is evaluated using the signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR) and mean square error (MSE). The results show that the Daubechies wavelet family of the tenth order with first and second of the levels of decomposition are the most optimal parameters for MRI liver image denoising.

Analysis of Performance of Two Wavelet Families using GLCM Feature Extraction for Mammogram Classification of Breast Cancer

: Mammography is the technique to detect breast cancer abnormal tissues using digital screening. It is the most efficient method to detect the cancerous tissues in the breast. But as the data for detecting, the abnormal tissue is very large, so it is a very inappropriate method for some radiologists to detect the abnormal tissues correctly. Therefore, computer-aided diagnosis is useful for detecting the cancerous tissues. For this, feature extraction and selection is considered an important and efficient method for mammogram classification of breast cancer. In this proposed work, the focus is made on wavelet family performance named db8 and bior3.7 used for extracting the features using GLCM feature extraction technique and 27 texture features are extracted at each level of decomposition and then the classification is done using different classifiers named Fuzzy-NN, Naive Bayes, MLP and Genetic programming. After this, the feature selection method is also applied to the extracted features named as PCA and Wavelet and then the comparison is made with different classification algorithms for both the wavelet family.