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Implementing Contourlet Transform for Medical image Fusion on a Heterogenous Platform

Implementing Contourlet Transform for Medical Image Fusion o...

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International conference on Scalable Computing and Communications;International conference on Embedded Computing, SCALCOM-EMBEDDEDCOM

作者： Yipeng Wei Yongxin Zhu Feng Zhao Yuehua Shi Tingting Mo Xiaoyi Ding Jie Zhong School of Microelectronics Shanghai Jiaotong University China Ruijin Hospital Shanghai Jiaotong University China

The classic wavelet transform has been applied in registration and fusion of medical images for decades. An extension namely, contourlet transform recently indicated its advantages in image fusion with better efficiency in multi-resolution and multi-direction representation and calculation. However, then even higher computational complexity it requires turns out to be a disastrous concern in embedded applications. In this paper, we implement an acceleration system on a heterogeneous TI Da vinci dual core processor consisting of an ARM processor core and a DSP processor core. The ARM core controls the fusion procedure by extracting the luminance bits and invoking the DSP core to carry out the time-consuming part of the contourlet transform. The partitions of tasks are determined after the program is analyzed and profiled at functional level to make full use of the computational capability of the heterogeneous platform. Initial measured improved performance results are obtained and analyzed with projected further improvements.

关键词： Biomedical imaging image fusion Frequency wavelet transforms Digital signal processing Computational complexity Embedded computing Partitioning algorithms Military computing Magnetic resonance imaging

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An Efficient High-Speed Lifting Based 1D/2D-DWT VLSI Architecture Using CDF-5/3 wavelet Transform For image processing applications

An Efficient High-Speed Lifting Based 1D/2D-DWT VLSI Archite...

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IEEE International conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)

作者： M Sushmitha S Chetan Sayantam Sarkar VLSI Design and Embedded Systems Dr. Ambedkar Institue of Technology Bengaluru India Dr. Ambedkar Institue of Technology Bengaluru India Vijaya Vittala Institue of Technology Bengaluru India

ISBN: (数字)9781728197722

ISBN: (纸本)9781728197739

There are various Discrete wavelet Transform architectures that are designed to fulfil certain requirements and criteria's. The convolution method which is an old traditional method which requires more multipliers, hardware resources and huge memory storage which is not apt to yield high speed and efficient image processing, signal processing application designs when compared to lifting method. In this paper, we have proposed an architecture for lifting scheme based CDF-5/3 2D-DWT, which includes less mathematical computations and high speed is achieved compared to existing works. The proposed architecture design is multiplier-less and utilizes lesser number of components hence it is area efficient and also achieves high throughput rate. The RTL of this design is described in VHDL language and is synthesized in Xilinx 14.5 and target device is virtex-5 series XC5VLX110T for testing the resulting parameters like LUTs, flip-flops and slices. Higher PSNR values and lesser MSE values are achieved when tested for different kind of images. The maximum operating frequency achieved for the designed 2D-DWT architecture is 258.358MHz. Hence the proposed 2D-DWT architecture has increased speed, less computational complexity and good hardware utilization when compared to other existing works.

关键词： Discrete wavelet transforms Finite impulse response filters image coding Two dimensional displays Very large scale integration Convolution Clocks

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Emboli Detection by machine learning algorithms using multi-level feature extraction

Emboli Detection by machine learning algorithms using multi-...

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International conference on image and signal processing and their applications (ISPA)

作者： Ab Waheed Lone Ahmet Elbir Nizamettin Aydin Department of Computer Engineering Yildiz Technical University Istanbul Turkey Department of Computer Engineering Istanbul Technical University Istanbul Turkey

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

In real world, data understanding involves finding relationship between dependent and independents events, and usually this data is embedded in the form of images, audio, videos, speech, text and much more and to extract useful and representative information, feature extraction and representation is a front-end step. For machine learning algorithms, feature extraction is a preprocessing technique that helps in finding the relationship between different variables. In biomedical signal analysis such as cerebral emboli signal detection, feature extraction from samples constitutes an important step. In this paper, we performed two step feature extraction procedure. First we extracted Mel Frequency Cepstral Coefficients (MFCC) and biologically inspired Gamma-tone Cepstral Coefficients from Doppler signals. Second we extracted kurtosis and skewness parameters from all the Mel-frequency cepstral coefficients and Gamma-tone Cepstral Coefficients coefficients. To gain the understanding of extracted features from Doppler signals, we trained some machine learning algorithms such as k-nearest neighbors, support vector machines and logistic regression. GTCC based kurtosis and skewness features show better classification between emboli, artifact signals and Doppler speckle signals. We present evaluation results using confusion matrix for classification between emboli signals (ES), Doppler speckle (DS) and artifact signals (AS).

关键词： wavelet transforms Machine learning algorithms Scattering Pattern classification Machine learning Speckle Feature extraction

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Comparison of higher order diffusion filters for speckle noise reduction

Comparison of higher order diffusion filters for speckle noi...

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International conference on Computer applications and Industrial Electronics (ICCAIE)

作者： Ch. Kranthi Rekha V. vijaya B. Sreedevi LUC Mantin Malaysia Vijay College of Engineering for Women VCEW India

visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used in the applications. image denoising involves the manipulation of the image data to produce a visually high quality image. This thesis reviews the existing denoising algorithms, such as filtering approach, wavelet based approach, and multifractal approach, and performs their comparative study. Different noise models including additive and multiplicative types are used. They include Gaussian noise, salt and pepper noise, speckle noise and Brownian noise. Selection of the denoising algorithm is application dependent. Hence, it is necessary to have knowledge about the noise present in the image so as to select the appropriate denoising algorithm. The filtering approach has been proved to be the best when the image is corrupted with salt and pepper noise. The Partial differential equation approach finds applications in denoising images corrupted with Gaussian noise. In the case where the noise characteristics are complex, the multi fractal approach can be used. A quantitative measure of comparison is provided by the signal to noise ratio of the image. Aim of the project is to reduce the speckle noise from the medical images by retaining most of the information, using fourth order partial differential equations and comparing the results with second order partial differential equation method, that is best suitable for a medical practitioner or a doctor to diagnose well by comparing this denoised image with the obtained.

关键词： Adaptive filters Speckle PSNR Filtering theory Partial differential equations image edge detection

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Remote sensing and acoustic telemetry

Remote sensing and acoustic telemetry

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OCEANS

作者： D. Green T. Nguyen Benthos Inc. North Falmouth MA USA ECE Department University of California San Diego CA USA

There is a growing need for remote underwater sensing and imaging without the burden of cabled connectivity to the outside world, and the best method for achieving this is with acoustic communications. However, the acoustic link is only one aspect of the problem, as the sensor system needs to interface with the modem, and perhaps with intermediary subsystems to provide some level of data pre-processing. We are developing an integrated processor/modem capability to provide the sensor developer with much of the signal processing capability needed to prepare and store sensor-level data, and to transmit information and receive commands as required. Our prototype development for such a system is focused on still-frame image compression and transmission in support of robotic crawlers in a mine-countermeasure (MCM) mission. image compression is based on novel wavelet-based algorithms developed in part by the University of California at San Diego (UCSD). The robotic crawlers are a development of Foster-Miller Inc. and the Naval Coastal Systems Station. Benthos provides the acoustic and RF links for command and control and image/sensor transmission. The sensor computer which performs the image compression is a micro-PC with extensive capabilities for R and D applications, including sensor-level data processing, in-situ data fusion, and connectivity to the modem. The modem currently supports signaling up to 2400 bps (raw data rate), although 100 bps to 800 bps are more likely to be used in other than vertical channels. We show examples of full and compressed images, the latter as received via an acomms-to-RF modem gateway buoy. Enhancements expected include incorporation of much higher data rate signaling methods, and customized signal processing for raw sensor-level data.

关键词： Remote sensing Telemetry Modems image coding Underwater acoustics Acoustic sensors Robot sensing systems Crawlers image sensors signal processing

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TCIE-ResNeXtGAN: infrared and water vapor satellite image fusion model using dual discriminator generative adversarial networks and ResNeXt for improving tropical cyclone intensity estimation accuracy

TCIE-ResNeXtGAN: infrared and water vapor satellite image fu...

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2022 conference on Infrared, Millimeter, Terahertz Waves and applications, IMT 2022

作者： Guo, Jia-Xu Zhang, Chang-Jiang College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Taizhou University Taizhou318000 China

ISBN: (纸本)9781510662476

Different characteristics of satellite images are reflected in different channels, so the monitoring and early warning of meteorological disasters based on satellite image data of a single channel may not achieve satisfactory results. The infrared channel satellite image reflects the ground and cloud top infrared radiation or the temperature distribution. The water vapor channel satellite image reflects the spatial distribution of water vapor in the upper atmosphere. The two channel satellite images reflect atmospheric characteristics from different wavebands. This paper proposes an infrared and water vapor channel satellite image fusion model (TCIE-ResNeXtGAN) based on dual discriminator generative adversarial network and ResNeXt to improve the accuracy of tropical cyclone (Tropical Cyclone, TC) intensity estimation. For this reason, we define the factors affecting TC intensity estimation, namely the brightness temperature gradient in satellite images, based on our previous work, and introduce it to the loss function in the proposed deep learning model to guide the training process of the model. In this way, the purpose of improving the estimation accuracy of TC intensity using fused satellite images is achieved. To demonstrate the effectiveness of the fusion results in this paper, we compare performance between the existing three models and our model. The experimental results show that the proposed image fusion method in this paper can preserve the infrared and water vapor dual-channel information to the greatest extent, while improving the estimation accuracy of TC intensity using fused satellite image. © 2023 SPIE.

关键词： Generative adversarial networks

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A Critical Analysis on EEG signal processing for BCI applications

A Critical Analysis on EEG Signal Processing for BCI Applica...

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Innovations in High Speed Communication and signal processing (IHCSP), International conference on

作者： Shashikant P. Patole Mamta Patankar vijayshri Chaurasia Madhu Shandilya Rajesh Mahadeva Praveen Sharma Department of Physics Khalifa University of Science and Technology Abu Dhabi UAE Department of Electronics & Communication Engineering Maulana Azad National Institute of Technology Bhopal India Department of CSE IES Institute of Technology and Management IES University Bhopal India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

An overview of the brain-computer interface is given in this publication and the variety of deep learning architectures for the acquisition of brain signals we have discussed the EEG signals that are used in BCI applications. Time-frequency localization is frequently poor in EEG signals. As a result, BCI systems frequently have low task detection accuracy and high error rates. It also exhibits extremely quasi qualities and has a lot of trans variances. Additionally, we have discussed noise removal techniques given by different researchers from EEG signals and BCI applications, and the comparison is given further than we have discussed regarding the EEG signal's image retrieval and different deep learning techniques for EEG learning commons. From the analysis of the results of different approaches, it’s been noticed that non-stationary EEG signals are more contributing to BCI applications. Whereas, in pre-processing steps, SWT-ICA, DWT, and CSP algorithms are most efficient for noise removal. For feature extraction sliding window spatial and temporal methods and deep learning methods contributed the most. Finally, for feature learning and classification, transfer learning and fine-tuned model performance were analyzed. It was observed from the analytical review that the fine-tuned transfer learning model outperforms better.

关键词： Deep learning Representation learning Reviews Transfer learning Noise Brain modeling Feature extraction Electroencephalography Brain-computer interfaces Discrete wavelet transforms

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A multi-scale energy detector for anomaly detection in dynamic networks

A multi-scale energy detector for anomaly detection in dynam...

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Asilomar conference on signals, Systems & Computers

作者： Arash Golibagh Mahyari Selin Aviyente Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA

ISBN: (纸本)9781479923915

Complex networks have attracted a lot of attention for representing relational data, where the weights of the edges, where edges' weights show the strength of relationships. Complex networks have found numerous applications in social and biological sciences. Studying the topology of these networks is important for a better understanding of the underlying systems and data. Currently, most of the network analysis tools are limited to static networks. However, most networks of interest have edges or relationships that vary across time. Therefore, there is a need to develop methods for the study of these dynamic networks. In this paper, we introduce another aspect of threat detection, which is identifying abrupt changes in edges' weights over time. wavelet decomposition method is used to separate the transient activity from the stationary activity in the edges. A hypothesis testing is proposed for the wavelet coefficients to detect any anomalous edges. Finally, the time points where the anomalous activity occurs are identified through the ratio of the energy of the anomalous to normal edges.

关键词： image edge detection wavelet packets Testing Media Data mining signal processing Social network services

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An FPGA Based Facial Component Extractor using wavelet-Based Salient Points

An FPGA Based Facial Component Extractor using Wavelet-Based...

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International conference on Information and Communication Technologies: From Theory to applications, ICTTA

作者： I. Hosseini M. Rahmaty M.S. Sadri N. Shams R.A. Fattahi ECE Department I.U.T. Iran

Facial component extraction is an active area of research and numerous methods are available for it. Some of these methods are slightly inaccurate because of vast dynamic characteristics of human face and its shape variations. However, these methods are widely used in real-time applications due to their low computational cost. In this paper we propose a new method that uses wavelet decomposition to extract salient points of the given image. Statistical analysis of the obtained salient points can exactly locate facial components. The proposed method tries to achieve computational efficiency and accuracy simultaneously. Simulation results present a detection rate of 95.6% for facial components. A complete FPGA implementation of the system is also proposed. It is shown that the logic resource usage for entire system is so low that the entire system can fit into a 300K gates Sparatan-IIE FPGA. The designed hardware is capable of extracting facial components for up to 5 frames per second

关键词： Field programmable gate arrays Principal component analysis Face detection Discrete wavelet transforms Computational efficiency Statistical analysis Hardware Face recognition Pattern recognition signal processing algorithms

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Overview of Artifacts Detection and Elimination Methods for BCI Using EEG

Overview of Artifacts Detection and Elimination Methods for ...

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International conference on image, vision and Computing (ICIVC)

作者： Clifford Maswanganyi Chunling Tu Pius Owolawi Shengzhi Du Department of Computer Systems Engineering Tshwane University of Technology Pretoria Gauteng RSA Department of Electrical Engineering Tshwane University of Technology Pretoria Gauteng RSA

ISBN: (纸本)9781538649923

Electroencephalogram (EEG) is the most commonly utilized method of mapping brain wave patterns from the scalp. It is a process of extracting electrical activities of the brain in the form of EEG signals using non-invasive methods. In EEG based techniques, a challenge exists in the form of signal contamination caused by internal and external factors in the recorded EEG signals., such as the artifacts, interferences among multiple EEG sources, and noise. signal pre-processing is the most significant phase for EEG based applications, for instance, the brain computer interface (BCI) systems. This is because the quality of data acquired from the brain through EEG measurements is dependent on the effectiveness of artifact detection and removal methods. During EEG recording there are several factors that influence the quality of data. One of the factors of signal contamination occurs as the result of physiological and non-physiological artifacts. To detect and remove such contaminations from EEG signals, there are several efficient artifact detection and elimination techniques based on certain pre-processing methods. The main objective of this study is to demonstrate various types of artifacts in EEG recordings as results of external and internal factors. Furthermore, the study will also present an extensive review of existing artifacts identification and elimination methods applied on EEG signals.

关键词： Electroencephalography Physiology Discrete wavelet transforms Electrodes Contamination Electric potential

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