Slantlet Transform Research Articles

Integrated Approach for ECG Signal Classification Using Fused Slantlet Transform and Autoregressive Features

Sudden cardiac death remains a significant concern, emphasizing the critical need for intelligent diagnostic techniques in cardiac disease detection. Cardiologic Vascular Diseases (CVD) pose a significant global health challenge, often associated with unhealthy lifestyle habits. Electrocardiogram (ECG) monitoring plays a crucial role in assessing heart function, but its interpretation can be challenging due to signal complexity. This study proposes a novel approach utilizing fused Slantlet-Spatial features alongside Auto Regression (AR) coefficients to classify electrocardiogram (ECG) signals. Leveraging the Slantlet Transform (SLT) for its superior time localization capabilities and the simplicity of AR coefficients, this methodology aims to enhance the accuracy of cardiac illness classification. Nineteen distinct features were extracted from 300 ECG signals, each comprising 512 samples, enriching the classification scheme's efficacy. The fused feature extraction technique demonstrated success in improving overall accuracy. Employing Binary Particle Swarm Optimization (BPSO), irrelevant features were pruned, resulting in a refined feature vector of only 14 relevant features. This reduction in feature dimensionality significantly influenced the classification accuracy. Subsequently, the refined feature vector was introduced to the classification stage, employing four distinct classifiers: Support Vector Machine (SVM) with various kernel functions, K-Nearest Neighbors (K-NN), Naïve Bayes (NB), and Decision Tree (DT). Simulation results revealed the feasibility of achieving an average classification accuracy of 99.67% across ECG signal categories. Notably, the polynomial kernel function-SVM classifier, with only 2-fold cross-validation, yielded optimal performance. This study emphasizes the potential of fused Slantlet-Spatial and AR features in enhancing the accuracy of cardiac disease classification. Moreover, the efficacy of BPSO in feature selection and the superior performance of the SVM classifier highlight promising avenues for further research in intelligent cardiac diagnosis systems. The proposed approach outperforms existing methods and contributes to the early detection of cardiovascular abnormalities, potentially improving patient outcomes.

High imperceptibility and robustness watermarking scheme for brain MRI using Slantlet transform coupled with enhanced knight tour algorithm

This research introduces a novel and robust watermarking scheme for medical Brain MRI DICOM images, addressing the challenge of maintaining high imperceptibility and robustness simultaneously. The scheme ensures privacy control, content authentication, and protection against the detachment of vital Electronic Patient Record information. To enhance imperceptibility, a Dynamic Visibility Threshold parameter leveraging the Human Visual System is introduced. Embeddable Zones and Non-Embeddable Zones are defined to enhance robustness, and an enhanced Knight Tour algorithm based on Slantlet Transform shuffles the embedding sequence for added security. The scheme achieves remarkable results with a Peak Signal-to-Noise Ratio (PSNR) evaluation surpassing contemporary techniques. Extensive experimentation demonstrates resilience to various attacks, with low Bit Error Rate (BER) and high Normalized Cross-Correlation (NCC) values. The proposed technique outperforms existing methods, emphasizing its superior performance and effectiveness in medical image watermarking.

Secure NIfTI Image Authentication Scheme for Modern Healthcare System

Advances in digital neuroimaging technologies, i.e., MRI and CT scan technology, have radically changed illness diagnosis in the global healthcare system. Digital imaging technologies produce NIfTI images after scanning the patient’s body. COVID-19 spared on a worldwide effort to detect the lung infection. CT scans have been performed on billions of COVID-19 patients in recent years, resulting in a massive amount of NIfTI images being produced and communicated over the internet for diagnosis. The dissemination of these medical photographs over the internet has resulted in a significant problem for the healthcare system to maintain its integrity, protect its intellectual property rights, and address other ethical considerations. Another significant issue is how radiologists recognize tempered medical images, sometimes leading to the wrong diagnosis. Thus, the healthcare system requires a robust and reliable watermarking method for these images. Several image watermarking approaches for .jpg, .dcm, .png, .bmp, and other image formats have been developed, but no substantial contribution to NIfTI images (.nii format) has been made. This research suggests a hybrid watermarking method for NIfTI images that employs Slantlet Transform (SLT), Lifting Wavelet Transform (LWT), and Arnold Cat Map. The suggested technique performed well against various attacks. Compared to earlier approaches, the results show that this method is more robust and invisible.

A Smart Content-Based Image Retrieval Approach Based on Texture Feature and Slantlet Transform

With the advancement of digital storing and capturing technologies in recent years, an image retrieval system has been widely known for Internet usage. Several image retrieval methods have been proposed to find similar images from a collection of digital images to a specified query image. Content-based image retrieval (CBIR) is a subfield of image retrieval techniques that extracts features and descriptions content such as color, texture, and shapes from a huge database of images. This paper proposes a two-tier image retrieval approach, a coarse matching phase, and a fine-matching phase. The first phase is used to extract spatial features, and the second phase extracts texture features based on the Slantlet transform. The findings of this study revealed that texture features are reliable and capable of producing excellent results and unsusceptible to low resolution and proved that the SLT-based texture feature is the perfect mate. The proposed method's experimental results have outperformed the benchmark results with precision gaps of 28.0 % for the Caltech 101 dataset. The results demonstrate that the two-tier strategy performed well with the successive phase (fine-matching) and the preceding phase (coarse matching) working hand in hand harmoniously.

X-Ray Image Authentication Scheme Using SLT and Contourlet Transform for Modern Healthcare System

The network’s convenience has created a copyright dilemma for some multimedia works. Nowadays, every healthcare system relies on digital medical images for diagnosis. These medical images are transmitted through communication channels, so there is a risk of tampering and copyright violation. A digital watermarking system can ensure and guarantee that tampering and copyright violation are prevented. This study presents a nonblind digital watermarking approach to X-ray medical images based on Contourlet transform (C.T.) and Slantlet Transform (SLT). Since the two-dimensional signals are represented flexibly by contourlet transforms, the contour plot can be used efficiently to represent curves and smooth contours. At the same time, the SLT has better time-localization & smoothness properties. The maximum energy of an image is conceived in the LL band if SLT transform are employed. Therefore, the LL band is used to entrench the watermark. The additive quantization method has been used to entrench the watermark. The efficiency of our scheme is assessed by different quality parameters and compared with several existing schemes. The results of the experiment show that the proposed scheme performs better and has the ability to resist several attacks.

A Robust Color Image Watermarking Scheme Based on Discrete Wavelet Transform Domain and Discrete Slantlet Transform Technique

Watermarking is primarily used to prevent unauthorized copying of digital data by inserting a watermark as a symbol of ownership in the digital data. The purpose of this work is to increase the imperceptibility and robustness of watermarked image by utilizing the Discrete Wavelet Transform (DWT) domain and Discrete Slantlet Transform (DST) technique. In compared to existing methodologies, the strategies used in this work considerably increased the robustness, imperceptibility, and protection of the watermarked image against JPEG compression and other types of noise attacks. The proposed method's robustness was determined by comparing the image's Normalized Cross-Correlation (NCC) value before and after the watermarking operation. The proposed method increased the NCC value to greater than 0.7 and the Peak Signal-to-Noise Ratio (PSNR) value to greater than 59 decibels (dB). Additionally, the proposed approach does not require knowledge of the original image in order to extract the watermarks.

A Novel Hybrid Reversible-Zero Watermarking Scheme to Protect Medical Image

The verification of copyright and authenticity for medical images is critical in telemedical applications. Watermarking is a key technique for protecting medical images and can be mainly divided into three categories: region of interest (ROI) lossless watermarking, reversible watermarking and zero-watermarking. However, ROI lossless watermarking causes biases on diagnosis. Reversible watermarking can hardly provide a continuous verification function and may face verification disputes after image recovering. Zero-watermarking requires third-party storage which may cause additional security problems. To address these issues, a hybrid reversible-zero watermarking (HRZW) is proposed in this paper to effectively combine the complementary advantages of reversible watermarking and zero-watermarking. In our scheme, a novel hybrid structure is designed including a zero-watermarking component and a reversible watermarking component. In the first component, ownership share is generated by mapping nearest neighbor grayscale residual (NNGR) based features and watermark information. In the second component, the generated ownership share is embedded reversibly based on Slantlet Transform, Singular Value Decomposition and Quantization Index Modulation (SLT-SVD-QIM). Experimental results demonstrate that our proposed scheme not only yields remarkable watermarking imperceptibility, distinguishability and robustness, but also provides continuous verification function without any dispute or third-party storage, which outperforms existing watermarking schemes for medical images.

Multivariable passive method for detection of islanding events in renewable energy based power grids

Penetration of distributed generation resources (DGR) in power grid is rapidly increasing to meet future energy demand efficiently. It helps in mitigating the problems of high carbon emission, green house effect, and increased cost of oil and natural gases. Island formation of a part of the renewable energy based power grid (REPG) creates hazards to operating personnel and equipments. Hence, efficient identification of islanding condition and disconnection of DGR immediately become essential for avoiding damage to equipment, personnel safety hazards and grid protection. This paper investigated a signal processing based passive islanding detection method (IDM) which is effective with the availability of multi-DGR. A islanding detection factor (MIDF) using multi-variables is proposed which is computed using features extracted by processing the voltage signal, negative sequence current (NSC) and negative sequence voltage (NSV) using Stockwell transform (ST) and Hilbert transform (HT). Total harmonic distortions of voltage ( T H D v $THD_{v}$ ) and current ( T H D i $THD_{i}$ ) are also considered to compute MIDF. Islanding event is recognized and differentiated from non-islanding conditions by comparing the MIDF with two threshold values. This method effectively detects the islanding event and discriminates it from the operational and faulty events. IDM has small non-detection zone (NDZ) and low computational time. Performance of IDM is not affected by noisy environment. Effectiveness of the IDM is established by testing for different event incidence angles, various fault impedances, different values of loads and capacitors, and different penetration levels of RE. Efficacy of IDM is established by testing the IDM through an elaborated study on IEEE-13 bus distribution grid interfaced with wind power plant (WPP) and solar power plant (SPP). Further, IDM is effectively implemented to detect the islanding event created on a practical distribution network. Effectiveness of IDM is better in comparison to IDMs designed by applications of discrete wavelet transform (DWT), empirical mode decomposition (EMD), Slantlet Transform and Ridgelet probabilistic neural network (RPNN), artificial neural network (ANN), change in rate of active and reactive power, rate of change of frequency, and rate of change of voltage angle.

A Slantlet based Statistical Features Extraction for Classification of Normal, Arrhythmia, and Congestive Heart Failure in Electrocardiogram

Intelligent and automated systems for diagnosing heart disease play a key role in treatment of heart disease and hence mitigating the possibility of heart disease, heart failure or sudden death. Thus, a Computer-Aided Design CAD system can provide a doctors with a clue about the category of patient heart disease, which might be Normal Sinus Rhythm, Abnormal Arrhythmia (ARR), and Congestive Heart Failure (CHF) electrocardiogram (ECG) signal. In this work a novel Slantlet transform (SLT) statistical features have been extracted and selected for 900 ECG segments taken from MIT-BIH ARR Database equally from three classes mentioned above for heart dieses classification through ECG signals. Based on the superiority of SLT in time localization as compared to the traditional wavelet transform, 12 out of 14 statistical features have been successfully passed the ANOVA test with P-value of 10−3. Then after, the relevant features are provided to three well-known classifiers (Support Vector Machine [SVM], K-nearest neighbor, and Naive Bayes). The performance tests show that Attaining 99.254% classification average AUC it can be achieved using SLT transform based features along with SVM classifier, which is a set of related supervised machine learning algorithm used for regression and classification with high generalization ability. It performs classification on two group problems. SVM classifier determines the best hyperplane which distinguishes between each positive and negative training sample.

Comparative Analysis of Image Steganography using SLT, DCT and SLT-DCT Algorithm

Steganography is an interesting science to be studied and researched at this time, because steganography is the science of hiding messages on other digital media so that other parties are not aware of the existence of information in the digital media. Steganography is very effective in maintaining information security, because the existence of this information is obscured so that it is difficult to know where it is. This paper discusses hiding text into images using the Slantlet Transform (SLT) method, Descreate Cosine Transform (DCT) and Hybrid of SLT and DCT. The three methods are implemented in the frequency domain where steganographic imagery is transformed from the spatial domain to the frequency domain and the message bit is inserted into the cover image frequency component. The comparison parameters of these three techniques are based on MSE, PSNR, Capacity & Robustness. From the results of the tests that have been done, it is obtained that the highest PSNR value is generated using the SLT-DCT method, the largest storage capacity is the SLT method while the resistance, SLT-DCT method and DCT method are more resistant to attack than the SLT method.

A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction.

Discrete wavelet transform (DWT) is often implemented by an iterative filter bank; hence, a lake of optimization of a discrete time basis is observed with respect to time localization for a constant number of zero moments. This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor. The MR images in the neutrosophic domain are defined using three membership sets, true (T), false (F), and indeterminate (I); then, SLT was applied to each membership set. Three statistical measurement-based methods are used to extract texture features from images of brain MRI. One-way ANOVA has been applied as a method of reducing the number of extracted features for the classifiers; then, the extracted features are subsequently provided to the four neural network classification techniques, Support Vector Machine Neural Network (SVM-NN), Decision Tree Neural Network (DT-NN), K-Nearest Neighbor Neural Network (KNN-NN), and Naive Bayes Neural Networks (NB-NN), to predict the type of the brain tumor. Meanwhile, the performance of the proposed model is assessed by calculating average accuracy, precision, sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate that the proposed approach is quite accurate and efficient for diagnosing brain tumors when the Gray Level Run Length Matrix (GLRLM) features derived from the composite NS-SLT technique is used.

SLT based watermarking system for secure telemedicine

In telemedicine, communication of medical images through emancipated systems similar to web is transcendent. While communicating, the medical images might be tampered by intruders. The specialist comprehensively hinge on the content of region of interest (ROI) in medical image for electing healing processes. To flee specious diagnosis, the professional needs to authorize the integrity of region of interest of the image formerly making diagnostic conclusions. Based on Slantlet transform (SLT) and singular value decomposition (SVD), this paper exhibits a novel watermarking framework for medical images to spot the altered areas inside ROI and reinstate the ROI. Tests directed utilizing this novel system exhibit that modified zones of ROI are recognized unequivocally and ROI of medicinal picture is reestablished.

Improved Steganography Techniques for Different Types of Secret Data

Steganography is an interesting aspect of data hiding art where the secret data is embedded in a cover media without raising any suspicion about the existence of that data. Over the years, different steganography techniques for digital images have been presented to be applied in different applications such as security and privacy control. Some of these techniques are based on transforming the image from the spatial domain to the transform domain as a pre-processing step before embedding the secret data. This paper presents an improved steganography technique in the transform domain based on studying the effect of changing two major factors on the performance of steganography techniques. The paper illustrates the algorithms that have been implemented for the study purposes. The results of this study proved that the two major factors that can affect the performance of the steganography technique are the transform type and the data embedding method. According to this study an improved steganography technique is presented based on Slantlet transform (SLT) and a new data embedding method. The experimental results proved the efficiency of the proposed technique in comparison with a previous SLT-based steganography technique.

A novel islanding detection technique using modified Slantlet transform in multi-distributed generation

In this paper, a new hybrid islanding detection scheme based on a combination of a modified Slantlet Transform (MSLT) and machine learning is applied to a passive time frequency islanding detection of multiple distributed generation units. A Harmony Search Algorithm (HSA) is used to optimally specify suitable scales of Slantlet transform and Slantlet decomposition levels for accurate islanding classification. Slantlet transform is utilized to derive the features in the required detection parameters measured from islanding and non-islanding events using identified Slantlet scales. In order to automate classification process, machine learning classifiers are utilized to detect islanding and non-islanding conditions with an objective of increasing the detection rate and avoiding nuisance distributed generation tripping during non-islanding situations. Islanding and non-islanding events are simulated for a multi-distributed generations system and used to assess the performance of the proposed anti-islanding protection method. The numerical results showing the efficiency of the proposed islanding detection technique are explained and conclusions are drawn.

Dual hybrid medical watermarking using walsh-slantlet transform

A hybrid robust lossless data hiding algorithm is proposed in this paper by using the Singular Value Decomposition (SVD) with Fast Walsh Transform (FWT) and Slantlet Transform (SLT) for image authentication. These transforms possess good energy compaction with distinct filtering, which leads to higher embedding capacity from 1.8 bit per pixel (bpp) up to 7.5bpp. In the proposed algorithm, Artificial Neural Network (ANN) is applied for region of interest (ROI) detection and two different watermarks are created. Embedding is done after applying FWH by changing the SVD coefficients and by changing the highest coefficients of SLT subbands. In dual hybrid embedding first watermark is the ROI and another watermark consists of three parts, i.e., patients’ personal details, unique biometric ID and the key for encryption. Comparison of the proposed algorithm is done with the existing watermarking techniques for analyzing the performance. Experiments are simulated on the proposed algorithm by casting numerous attacks for testing the visibility, robustness, security, authenticity, integrity and reversibility. The resultant outcome proves that the watermarked image has an improved imperceptibility with a high level of payload, low time complexity and high Peak Signal to Noise Ratio (PSNR) against the existing approaches.

An Enhancement of Data Hiding Imperceptibility using Slantlet Transform (SLT)

This study proposes a hybrid technique in securing image data that will be applied in telemedicine in future. Based on the web-based ENT diagnosis system using Virtual Hospital Server (VHS), patients are able to submit their physiological signals and multimedia data through the internet. In telemedicine system, image data need more secure to protect data patients in web. Cryptography and steganography are techniques that can be used to secure image data implementation. In this study, steganography method has been applied using hybrid between Discrete Cosine Transform (DCT) and Slantlet Transform (SLT) technique. DCT is calculated on blocks of independent pixels, a coding error causes discontinuity between blocks resulting in annoying blocking artifact. While SLT applies on entire image and offers better energy compaction compare to DCT without any blocking artifact. Furthermore, SLT splits component into numerous frequency bands called sub bands or octave bands. It is known that SLT is a better than DWT based scheme and better time localization. Weakness of DCT is eliminated by SLT that employ an improved version of the usual Discrete Wavelet Transform (DWT). Some comparison of technique is included in this study to show the capability of the hybrid SLT and DCT. Experimental results show that optimum imperceptibility is achieved.

Islanding detection technique using Slantlet Transform and Ridgelet Probabilistic Neural Network in grid-connected photovoltaic system

In this paper, a new islanding detection technique is proposed for a three-phase grid connected photovoltaic inverter system using the multi-signal analysis method. The proposed strategy is divided into two steps: first step, all possible grid faults, switching transients and islanding events are simulated and the essential detection parameters are measured. By means of the Slantlet Transform theory, the energy, mean value, minimum, maximum, range, standard deviation and log energy entropy at any decomposition level of Slantlet Transform for parameter detection is computed and the best of them are selected as input data of second step. Second step, an advanced machine learning based on Ridgelet Probabilistic Neural Network is utilized to predict islanding and none islanding states. In order to train Ridgelet Probabilistic Neural Network, a modified differential evolution algorithm with new mutation phase, crossover process, and selection mechanism is proposed. The results depicting the effectiveness of the proposed method are explained and outcomes are drawn.

Robust Video Watermarking of Hybrid Based Techniques

This paper adopted a new video watermarking scheme based on hybrid transforms to achieve the watermarking requirements, that is, robustness, imperceptibility, and security. Where In our system first of all, the original video will be divided into frames to transform it using the Slantlet Transform into four sub-bands (i.e., LL, LH, HL, and HH). Secondly, The HL sub-band will be chosen for further decomposition using Contourlet Transform (CT), and then the low sub-band of CT will be selected to decompose it to fixed size non overlapping blocks to employ the DCT (Discrete Cosine Transform) on each block. lastly, to improve the security and robustness, the watermark logo is scrambled using AT(Arnold transformation ) as a prelude to embed it’s on the each transformed non-overlapping blocks. Experimental results show that the proposed system achieves good imperceptibility and high resistance against various attacks.

Odia Handwritten Vowel Recognition System using Slantlet Transform and Differential Evolution based Functional Link Artificial Neural Network Classifier

One of the challenging problems in optical character recognition (OCR) is the identification of handwritten characters. It has several complexities like missing part, size variation, added noise, media devices used, slant variation etc. The goal of this paper is to develop an effective system for recognition of Odia handwritten vowels using functional link artificial neural network (FLANN) classifier and differential evolution (DE) optimization technique. In this paper the emphasis is on preprocessing, feature extraction, feature reduction and classification with optimization technique. Various preprocessing operations like size normalization mean filtering and canny edge detection methods are carried out on the vowel images to enhance the quality. Slantlet transform (SLT) is applied for feature extraction and the extracted features are further reduced by principal component analysis (PCA) method. FLANN classifier is applied on the reduced feature vector and the weights of the FLANN are optimized with DE evolutionary technique. The system has achieved a good classification accuracy of 91.32% on the dataset.

Computer Aided Diagnosis System for Breast Cancer using ID3 and SVM Based on Slantlet Transform

Lately, the woman's chest cancer is the 2nd reason of deaths in females. Mam.mog.ram images are medical images, which can be read by physicians to detect breast carcinomas. In this paper, proposed Computer Aided Diagnosis system that can assist the doctors in hospitals to improve the diagnosis of the disease to detect cancer cells. Enhance the undesirable effects the of mam.mog.ram images by using slantlet transformer , set of different stages and classifies as normal, abnormal according to ID3 and SVM. For the same testing set, the practical outcomes displays SVM classifier with an accuracy of 95% and ID3 classifier with an accuracy of 92% based on MIAS database.