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An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion

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BIOMEDICAL signal processing AND CONTROL 2021年第0期66卷 102480-102480页

作者： Jose, Jais Gautam, Neha Tiwari, Mohit Tiwari, Tripti Suresh, Arjun Sundararaj, Vinu Rejeesh, M. R. Amity Univ Amity Inst Geoinformat & Remote Sensing Noida India JAIN Deemed Univ Fac Engn & Technol Bengaluru Karnataka India Bharati Vidyapeeths Coll Engn Dept Comp Sci & Engn New Delhi India Bharati Vidyapeeth Deemed Univ Management Studies Pune Maharashtra India Amity Univ Amity Inst Environm Sci Noida India Anna Univ Dept Elect & Commun Chennai Tamil Nadu India

The fast-developing image fusion technique has become a necessary one in every field. Analyzing the efficiency of various fusion technologies analytically and objectively are spotted as an essentially required processes. Further, image fusion becomes an inseparable technique in the medical field, since the role of medical images in diagnosing and identifying diseases becomes a crucial task for the radiologists and doctors at its early stage. Different modalities used in clinical applications offer unique information, unlike any other in any form. To diagnose diseases with high accuracy, clinicians require data from more than one modality. Multimodal image fusion has received wide popularity in the medical field since it enhances the accuracy of the clinical diagnosis thereby fusing the complementary information present in more than one image. Obtaining optimal value along with a reduction in cost and time in multimodal medical image fusions are a critical one. Here, in this paper a new multi-modality algorithm for medical image fusion based on the Adolescent Identity Search Algorithm (AISA) for the Non-Subsampled Shearlet Transform is proposed to obtain image optimization and to reduce the computational cost and time. The NSST is a multi-directional and multi-dimensional example of a multiscale and multi-directional wavelet transform. The input source image is decomposed into the NSST subbands at the initial stage. The boundary measure is modulated by the Adolescent Identity Search Algorithm (AISA) that fuses the sub-band in the NSST thereby reducing the complexity and increasing the computational speed. The proposed method is tested under different real-time disease datasets such as Glioma, mild Alzheimer's, and Encephalopathy with hypertension that includes similar pairs of images and analyzed different evaluation measures such as Entropy, standard deviation, structural similarity index measure,Mutual information, Average gradient, Xydeas and Petrovic metric, Peak

关键词： Non-subsampled Shearlet transform Multi-modal medical image fusion Magnetic resonance PET AISA

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工程振动信号分析与处理若干技术

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图像与信号处理 2024年第4期13卷 378-393页

作者：黄伟国机集团科学技术研究院有限公司国机集团工程振动控制技术研究中心北京

对工程振动信号分析与处理中的常用技术进行了研究,包括消除趋势项、数字滤波、频域微积分、生成扫频与白噪声信号、模态参数识别、三分之一倍频程谱、小波分解与重构等,编制了相关程序,并基于实测振动信号进行了分析与处理,与相关商业... 详细信息

对工程振动信号分析与处理中的常用技术进行了研究,包括消除趋势项、数字滤波、频域微积分、生成扫频与白噪声信号、模态参数识别、三分之一倍频程谱、小波分解与重构等,编制了相关程序,并基于实测振动信号进行了分析与处理,与相关商业软件进行了对比校核。消除趋势项可以解决采集数据偏离基线、避免数据无法使用的问题;高通与低通滤波可以根据分析需要任意滤除某频段并获得对应时域数据,从而为振动控制提供理想输入;频域微积分可以通过间接采集加速度或速度信号,分析得到位移信号;扫频和白噪声信号可以为系统振动与隔振性能检验提供输入信号;随机减量法、NExT法、IDT法是系统模态参数识别的重要方法;三分之一倍频程谱是微振动、噪声控制等多领域评价的重要标准;小波分解与重构可以对多源振动信号进行分解,获得各频段成分的各类型振动信号。本研究中总结的若干技术是工程振动控制中最为常用的分析处理技术,对于实际工程应用具有重要的指导意义。The common technologies in the analysis and processing of engineering vibration signals are systematically studied, including the elimination of trend terms, digital filtering, calculus in frequency domain, generation of swept frequencies and white noise signals, modal parameter identification, one-third octave spectrum, wavelet decomposition and reconstruction, etc., and the related programs are compiled, which are analyzed and processed based on the measured vibration signals, which are compared with relevant commercial software. The elimination of trend terms can solve the problem of collecting data deviating from the baseline and avoiding data from being unusable. High-pass and low-pass filtering can arbitrarily filter out a certain frequency band and obtain the corresponding time-domain data according to the analysis needs, so as to provide ideal input for vibration control. Calculus in frequency domain can be used to indirectly analyze the displacement signals by the collected acceleration or velocity signals. The generation of swept frequencies and white noise signals can provide input signals for the vibration isolation performance test. The random reduction method, NExT method and IDT method are important methods for modal parameter identification. One-third octave spectrum is an important criterion for the evaluation of micro-vibration, noise control and other fields. wavelet decomposition and reconstruction can decompose multi-source vibration signals to obtain various types of vibration signals with a variety of frequency band components. The techniques summar

关键词：消除趋势项数字滤波频域微积分扫频与白噪声信号模态参数识别三分之一倍频程谱小波分解与重构

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A Parallel Strategies for 2-D Lifting wavelet Transform Using GPU 12

A Parallel Strategies for 2-D Lifting Wavelet Transform Usin...

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12th International Congress on image and signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Wang, Lei Liu, Hongying Sun, Li Zhou, Mei Zhuang, Quanjie Shanghai Key Laboratory of Multidimensional Information Processing Communications and Electronic Engineering Institute East China Normal University Shanghai China Shanghai Lanche Biological Technology Co. Ltd. Shanghai China

ISBN: (纸本)9781728148526

Lifting wavelet Transform has a huge number of applications in image-processing techniques, it has been applied in field like image compression, fusion, de-noising, et al, but for large scale image, the algorithm is too slow to meet the real-time requirements. To solve this problem, this work proposed a parallel strategy based on GPU for 2-D lifting wavelet transform. Through the performance test and analysis, the proposed lifting scheme achieve considerable speedups compared with CPU version for the same image size. © 2019 IEEE.

关键词： Graphics processing unit

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Combining Scatter Transform and Deep Neural Networks for Multilabel Electrocardiogram signal Classification

Combining Scatter Transform and Deep Neural Networks for Mul...

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Computing in Cardiology Conference (CinC)

作者： Oppelt, Maximilian P. Riehl, Maximilian Kemeth, Felix P. Steffan, Jan Fraunhofer IIS Dept Image Proc & Med Engn Wolfsmantel 33 D-91058 Erlangen Germany Johns Hopkins Univ Dept Chem & Biomol Engn Whiting Sch Engn Baltimore MD 21218 USA

ISBN: (纸本)9781728173825

An essential part for the accurate classification of electrocardiogram (ECG) signals is the extraction of informative yet general features, which are able to discriminate diseases. Cardiovascular abnormalities manifest themselves in features on different time scales: small scale morphological features, such as missing P-waves, as well as rhythmical features apparent on heart rate scales. For this reason we incorporate a variant of the complex wavelet transform, called a scatter transform, in a deep residual neural network (ResNet). The former has the advantage of being derived from theory, making it well behaved under certain transformations of the input. The latter has proven useful in ECG classification, allowing feature extraction and classification to be learned in an end-to-end manner. Through the incorporation of trainable layers in between scatter transforms, the model gains the ability to combine information from different channels, yielding more informative features for the classification task and adapting them to the specific domain. For evaluation, we submitted our model in the official phase in the PhysioNet/Computing in Cardiology Challenge 2020. Our (Team Triage) approach achieved a challenge validation score of 0.640, and full test score of 0.485, placing us 4th out of 41 in the official ranking.

关键词： Classification (of information)

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A Generalized Multifractal Formalism for the Estimation of Nonconcave Multifractal Spectra

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IEEE TRANSACTIONS ON signal processing 2019年第1期67卷 110-119页

作者： Leonarduzzi, Roberto Abry, Patrice Wendt, Herwig Jaffard, Stephane Touchette, Hugo Univ Claude Bernard Univ Lyon ENS Lyon CNRSLab Phys F-69342 Lyon France Univ Toulouse CNRS UMR 5505 IRIT F-31000 Toulouse France Univ Paris Est Lab Anal & Math Appl CNRS UMR 8050 UPEC F-94010 Creteil France Stellenbosch Univ Natl Inst Theoret Phys NITheP ZA-7600 Stellenbosch South Africa Stellenbosch Univ Dept Phys ZA-7600 Stellenbosch South Africa

Multifractal analysis has become a powerful signal processing tool that characterizes signals or images via the fluctuations of their pointwise regularity, quantified theoretically by the so-called multifractal spectrum. The practical estimation of the multifractal spectrum fundamentally relies on exploiting the scale dependence of statistical properties of appropriate multiscale quantities, such as wavelet leaders, that can be robustly computed from discrete data. Despite successes of multifractal analysis in various real-world applications, current estimation procedures remain essentially limited to providing concave upper-bound estimates, while there is a priori no reason for the multifractal spectrum to be a concave function. This paper addresses this severe practical limitation and proposes a novel formalism for multifractal analysis that enables nonconcave multifractal spectra to be estimated in a stable way. The key contributions reside in the development and theoretical study of a generalized multifractal formalism to assess the multiscale statistics of wavelet leaders, and in devising a practical algorithm that permits this formalism to be applied to real-world data, allowing for the estimation of nonconcave multifractal spectra. Numerical experiments are conducted on several synthetic multifractal processes as well as on a real-world remote-sensing image and demonstrate the benefits of the proposed multifractal formalism over the state of the art.

关键词： Multifractal analysis nonconcave multifractal spectrum wavelet leaders Legendre transform generalized canonical ensemble

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An evolutionary computing enriched RS attack resilient medical image steganography model for telemedicine applications

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MULTIDIMENSIONAL SYSTEMS AND signal processing 2019年第2期30卷 791-814页

作者： Mansour, Romany F. Abdelrahim, Elsaid M. Assiut Univ Fac Sci Dept Math Assiut Egypt Tanta Univ Fac Sci Dept Math Comp Sci Div Tanta Egypt

The recent advancement in computing technologies and resulting vision based applications has given rise to a novel practice called telemedicine that requires patient diagnosis images or allied information to recommend or even perform diagnosis practices being located remotely. However, to ensure accurate and optimal telemedicine there is the requirement of seamless or flawless biomedical information about patient. On the contrary, medical data transmitted over insecure channel often remains prone to manipulated or corrupted by attackers. The existing cryptosystems alone are not sufficient to deal with these issues and hence in this paper a highly robust reversible image steganography model has been developed for secret information hiding. Unlike traditional wavelet transform techniques, we incorporated Discrete Ripplet Transformation technique for message embedding in the medical cover images. In addition to, ensure seamless communication over insecure channel, a dual cryptosystem model containing proposed steganography scheme and RSA cryptosystem has been developed. One of the key novelties of the proposed research work is the use of adaptive genetic algorithm for optimal pixel adjustment process that enriches data hiding capacity as well as imperceptibility features. The performance assessment reveals that the proposed steganography model outperforms other wavelet transformation based approaches in terms of high PSNR, embedding capacity, imperceptibility etc.

关键词： Medical image steganography Ripplet transform Adaptive genetic algorithm OPAP Telemedicine

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Discrete wavelet transform for generative adversarial network to identify drivers using gyroscope and accelerometer sensors

TechRxiv

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TechRxiv 2021年

作者： Ahmadian, Ruhallah Ghatee, Mehdi Wahlström, Johan Department of Computer Science Amirkabir University of Technology Tehran15875-4413 Iran Department of Computer Science Amirkabir University of Technology Tehran15875-4413 Iran Department of Computer Science University of Exeter ExeterEX4 4QF United Kingdom

Driver identification is an important research area in intelligent transportation systems, with applications in commercial freight transport and usage-based insurance. One way to perform the identification is to use smartphones as sensor devices. By extracting features from smartphone-embedded sensors, various machine learning methods can identify the driver. The identification becomes particularly challenging when the number of drivers increases. In this situation, there is often not enough data for successful driver identification. This paper uses a Generative Adversarial Network (GAN) for data augmentation to solve the problem of lacking data. Since GAN diversifies the drivers’ data, it extends the applicability of the driver identification. Although GANs are commonly used in image processing for image augmentation, their use for driving signal augmentation is novel. Our experiments prove their utility in generating driving signals emanating from the Discrete wavelet Transform (DWT) on smartphones’ accelerometer and gyroscope signals. After collecting the augmented data, their histograms along the overlapped windows are fed to machine learning methods covered by a Stacked Generalization Method (SGM). The presented hybrid GAN-SGM approach identifies drivers with 97% accuracy, 98% precision, 97% recall, and 97% F1-measure that outperforms standard machine learning methods that process features extracted by the statistical, spectral, and temporal approaches. © 2021, CC BY.

关键词： Smartphones

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Morphological and Temporal ECG Features for Myocardial Infarction Detection Using Support Vector Machines 8th

Morphological and Temporal ECG Features for Myocardial Infar...

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8th Latin American Conference on Biomedical Engineering (CLAIB) / 42nd National Conference on Biomedical Engineering (CNIB)

作者： Arenas, Wilson J. Sotelo, Silvia A. Zequera, Martha L. Altuve, Miguel Pontificia Univ Javeriana Elect Dept Sch Engn BASPI FootLabBioengn Signal Anal & Image Proc Re Bogota DC Colombia Pontifical Bolivarian Univ Dept Basic Sci Bucaramanga Colombia Pontifical Bolivarian Univ Fac Elect & Elect Engn Bucaramanga Colombia

ISBN: (纸本)9783030306489;9783030306472

Myocardial infarction is a leading cause of death worldwide. A 12-lead electrocardiogram (ECG) recording is commonly performed to diagnose this pathology. In this paper, we explored temporal and morphological features extracted from multi-lead ECG signals to classify subjects from the PTB Diagnostic ECG database into healthy control and myocardial infarction using a support vector machine binary classifier. After delineating the 12-lead ECG signals with a wavelet transform-based method, a unique set of characteristic points was obtained for the ECG leads by suppressing outliers and by taking the average of the remaining points. Then, mathematical operations (average, standard deviation, skewness, etc.) performed to the P wave duration, QRS complex duration, ST-T complex, QT interval, T wave duration and RR interval were used as temporal features, and mathematical operations performed to ECG signals bounded by the P wave, QRS complex, ST-T complex and QT interval were used as morphological features. A 10-fold Monte Carlo cross-validation was employed to analyze the reproducibility of the classification results by randomly splitting the dataset into training (70%) and test (30%) sets with balanced classes. Mean classification accuracies above 93% were achieved when the SVM classifier uses only temporal ECG features, only morphological ECG features, and both temporal and morphological ECG features. The best classification performance was achieved when temporal and morphological ECG features are jointly considered by the binary SVM classifier (accuracy 96.67%, error rate 3.33%, sensitivity 97.33% and specificity 96.00%).

关键词： Digital signal processing Electrocardiography Myocardial Infarction Support vector machines

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Infrared and visible image fusion based on oversampled graph filter banks

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JOURNAL OF ELECTRONIC IMAGING 2020年第2期29卷 023016-023016页

作者： Song, Chunyan Gao, Xueying Qiao, Yulong Zhang, Kaige Northeast Forestry Univ Coll Informat & Comp Engn Harbin Heilongjiang Peoples R China Harbin Engn Univ Coll Informat & Commun Engn Harbin Heilongjiang Peoples R China Utah State Univ Dept Comp Sci Logan UT 84322 USA

The infrared image (RI) and visible image (VI) fusion method merges complementary information from the infrared and visible imaging sensors to provide an effective way for understanding the scene. The graph filter bank-based graph wavelet transform possesses the advantages of the classic wavelet filter bank and graph representation of a signal. Therefore, we propose an RI and VI fusion method based on oversampled graph filter banks. Specifically, we consider the source images as signals on the regular graph and decompose them into the multi-scale representations with M-channel oversampled graph filter banks. Then, the fusion rule for the low-frequency subband is constructed using the modified local coefficient of variation and the bilateral filter. The fusion maps of detail subbands are formed using the standard deviationbased local properties. Finally, the fusion image is obtained by applying the inverse transform on the fusion subband coefficients. The experimental results on benchmark images show the potential of the proposed method in the image fusion applications. (C) 2020 SPIE and IS&T

关键词： image fusion infrared image graph filter banks graph signal processing

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Design of Symlet wavelet based Illumination Normalization Algorithm and its Comparison with other Relevant Algorithms 42

Design of Symlet Wavelet based Illumination Normalization Al...

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42nd International Conference on Telecommunications and signal processing (TSP)

作者： Lamichhane, Kamal Mazumdar, Pramit Univ Roma La Sapienza Rome Italy Univ Studi Roma TRE Rome Italy

ISBN: (纸本)9781728118642

image processing techniques may be used for enhancing edges, boundaries, contrast, etc. of an image through accentuation or sharpening process. Many algorithms are available to normalize illumination impact for different image processing applications. In this contribution, we conduct a comparative study on four different types of illumination normalization algorithms. They are based on discrete wavelet, logarithmic total variation with primal dual algorithm, histogram equalization technique, and morphological operation. In order to obtain better enhancement of image by using discrete wavelet based illumination normalization algorithm, selection of the particular wavelet is very important. Use of histogram equalization function in image preprocessing with other algorithms enhances the overall performance of face detection. This paper illustrates performance of different illumination normalization algorithms in Viola-Jones face detection system based on the extended Yale B database.

关键词： Discrete wavelet transform Face detection Histogram equalization Illumination normalization Viola-Jones algorithm

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