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检索条件"机构=Key Lab of Image Processing and Image Communication"

共 885 条记录，以下是461-470 订阅

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Beta wave of sleep electroencephalogram analysis based on sign series entropy

Beta wave of sleep electroencephalogram analysis based on si...

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2016 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2016)

作者： Min Zhao Yuting Li Lu Yang Jia-Fei Dai Jun Wang Feng-Zhen Hou Image Processing and Image Communications Key Lab. Nanjing Univ.of Posts & Telecomm. Nanjing General Hospital of Nanjing Military Command School of Science China Pharmaceutical University

ISBN: (纸本)9781510823808

Sleep and wake EEG have some *** studying their brain waves and calculating the sign series entropy,we use the T test for the detection of sleep and wake EEG data to figure out whether they are *** beta waves being filtered out by the filter,we calculate the entropy of the sign *** results of T test show that the beta waves in the state of sleep and wake are different.

关键词： sleep EEG sign series entropy

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Beta Wave of Sleep Electroencephalogram Analysis Based on Multiscale Sign Series Entropy

Beta Wave of Sleep Electroencephalogram Analysis Based on Mu...

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2015 International Conference on Artificial Intelligence and Industrial Engineering(AIIE 2015)

作者： Jianhui Jiang Shitong Wang Fengzhen Hou Jun Wang Jin Li Image Processing and Image Communications Key Lab. Nanjing Univ.of Posts & Telecomm School of Science China Pharmaceutical University College of Physics and Information Technology Shaanxi Normal University

ISBN: (纸本)9781510806450

Sleep Electroencephalogram(Sleep EEG) detection and treatment can provide the basis for clinical diagnosis and treatment. According to the non-stationary random character of EEG itself, the paper proposed multiscale sign series entropy(MSSE) method and applied it to the state of sleep EEG analysis. Numerical results showed that, MSSE method can effectively differentiate awake period β wave and sleep stage β wave even if under the influence of the noise. The results show that the algorithm can aid in clinical diagnosis of sleep EEG.

关键词： sleep electroencephalogram multiscale sign series entropy clinical diagnosis

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Sleep EEG analysis Based on the Multiscale Jenson-Shannon Divergence

Sleep EEG analysis Based on the Multiscale Jenson-Shannon Di...

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2016 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2016)

作者： Zhengxia Zhang Jia-Fei Dai JunWang Feng-Zhen Hou Image Processing and Image Communications Key Lab. Nanjing Univ.of Posts & Telecomm. Nanjing General Hospital of Nanjing Military Command School of Science China Pharmaceutical University

ISBN: (纸本)9781510823808

Sleep EEG signals analysis is a hotspot of research recently,this paper,by using nonlinear dynamics theory knowledge,JSD algorithm and multi-scale JSD algorithm is proposed for some individual conscious period and NREM sleep stage I analyzed the research of EEG signals,and the use of SPSS statistical software to verify the veracity and reliability of the experiment,at the same time,with the error bar graph method to analysis the two different states of sleep EEG signals,the results show that both the JSD algorithm and the multi-scale JSD algorithm can effectively distinguish between awake and NREM sleep stage I of EEG signals,these two conditions' EEG signals exist significant differences,The algorithm we proposed can be further used in the study of sleep EEG in installment,which can also provide all kinds of disease diagnosis and treatment of sleep with effective auxiliary function,the research has important practical significance in the future.

关键词： multi-scale JSD sleep EEG signals

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Automated Anemia Classification and Hemoglobin Level Prediction using Deep CNN and GLCM Features of Palpebral Conjunctiva images

Automated Anemia Classification and Hemoglobin Level Predict...

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Conference on Information and communication Technology (CICT)

作者： Chandrasekhar Bhusham Ajay Kumar Reddy Poreddy Thunakala Bala Krishna Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

Anemia is a common medical condition affecting millions worldwide, particularly in developing countries. Early detection of anemia is crucial for prompt treatment and prevention of its potential complications. In recent years, deep learning (DL) has shown great potential in various medical applications, including medical image classification, anomaly detection, and segmentation. This study proposes a transfer learning-based approach using a pre-trained DL model to detect anemia from palpebral conjunctiva images. The proposed method utilizes a pre-trained DenseNet-201 model and fine-tuned it on a target dataset of palpebral conjunctiva images to detect anemia. Deep features of palpebral conjunctiva images computed from the fine-tuned DenseNet-201 are fed to MLP to identify anemia. The performance of the proposed method is evaluated on a publicly available anemia dataset, and the results show that the proposed method achieves an accuracy of 93.7 % in detecting anemia from palpebral conjunctiva images. In addition to anemia classification, we computed the hemoglobin level of palpebral conjunctiva images based on the gray-level co-occurrence matrix (GLCM) statistical properties. The statistical properties of GLCM are given to support vector and polynomial regressors, and the mean value of the predicted scores of both regressors is used to estimate the hemoglobin level. Experimental results show that the proposed model achieves an average root mean square error of 0.72 for conjunctiva images.

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Terahertz compressive imaging: understanding and improvement by a better strategy for data selection

Terahertz compressive imaging: understanding and improvement...

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作者： Xing, Chungui Qi, Feng Liu, Zhaoyang Wang, Yelong Guo, Shuxu State Key Laboratory on Integrated Optoelectronics College of Electronic Science and Engineering Jilin University Changchun China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang China Key Lab of Image Understanding and Computer Vision Shenyang China

Compressive sensing (CS) is a novel sampling modality, which indicates the signals can be sampled at a rate much below the Nyquist sampling rate. CS has increasing interest recently due to high demand of rapid, efficient, and in-expensive signal processing applications in the μmWave and mmWave frequencies, such as communication and imaging. There have been a lot of theoretical studies on this topic, but there is a lack of systematic experimental analysis of the implementation method itself. In this paper, we have investigated the influencing factors of terahertz compressive sensing based on experimental results, including illumination and the size of the pixel. Besides, to differentiate from current approaches, which generally make full use of the data, we propose to sort the data first and select a part of them based on amplitude, which might deliver a better image by prompting the mathematical calculations compulsively. We believe that such considerations given above would help to make a better system design and improve the performance of compressive imaging, and these results will also be helpful in the application of terahertz communication. © 2021 John Wiley & Sons, Ltd

关键词： Compressed sensing

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Fetal Electrocardiogram Extraction Based On Modified Robust Independent Component Analysis

Fetal Electrocardiogram Extraction Based On Modified Robust ...

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2013 International Conference on Bio-Medical Materials and Engineering(ICBME 2013)

作者： Wenpo Yao Junchang Zhao Zhengzhong Zheng Tiebing Liu Hongxing Liu Jun Wang Image Processing and Image Communications Key Lab. Nanjing Univ. of Posts & Telecomm Nanjing General Hospital of Nanjing Military Command School of Electronic Science and Engineering Nanjing University

Fetal electrocardiogram(FECG) separation gets widely attention due to its clinical significance In the paper, we proposed an improved robust independent component analysis for fetal ECG separation Firstly, wavelet decomposition was applied to fetal ECG to get the relevant parameters Then, the Robust ICA was used to separate the mixed signals Compared to robust independent component analysis, computing speed of the improved algorithm increased by an average of 15 percent while minimum mean square error fluctuations 00008, which indicated that this algorithm could be effectively used in clinical fetal ECG monitoring

关键词： Robust ICA Wavelet Transform FECG Kurtosis

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Alpha Wave Attention EEG Analysis Based on the Multiscale Jensen-Shannon Divergence

Alpha Wave Attention EEG Analysis Based on the Multiscale Je...

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2014 2nd International Conference on Materials Science and Mechanical Engineering(2nd ICMSME 2014)

作者： Zhengxia Zhang Siqiu Xu Erning Zhou Xiaolin Huang Jun Wang Image Processing and Image Communications Key Lab College of Geo & Bio InformationNanjing Univ of Posts & Telecomm Institute of Biomedical Electronic Engineering School of Electronic Science and EngineeringNanjing University

The article adopted the multiscale Jensen- Shannon Divergence method for EEG complexity analysis. Then the study found that the method can distinguish between three different status(eyes closed, count, in a daze) acquisition of Alpha EEG time series, which shows three different states of Alpha EEG time series having significant differences. In each state of the three different states(eyes closed, count, in a daze), we aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series proxy data, finding that there are large amounts of nonlinear time series in the Alpha EEG signals. This method is also fully proved that the multiscale JSD algorithm can be used to analyze EEG signals. Attention statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future.

关键词： Multiscale JSD Attention Alpha EEG signals

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Noise and performance analysis on fundus images with CNN and transformer models

Noise and performance analysis on fundus images with CNN and...

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Conference on Information and communication Technology (CICT)

作者： Niranjana Vannadil Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

Fundus imaging is a valuable diagnostic tool in ophthalmology, providing clinicians with detailed visualizations of the retina and aiding in the detection and monitoring of various eye diseases, including age-related macular degeneration (AMD), glaucoma, diabetic retinopathy (DR), and cataract. However, the quality of fundus images can be significantly affected by noise, mainly additive white Gaussian noise (AWGN), which is inherent in many imaging systems. The presence of noise in real-world data poses significant challenges for computer vision tasks. In the field of medical image classification, a wrong diagnoisis has heavy consequences. Understanding the impact of AWGN on fundus images is crucial for developing practical denoising algorithms and improving diagnostic accuracy. This work presents an analysis of AWGN noise in fundus images aims to characterize its effects on image quality and assess its impact on diagnostic tasks. The work also analyzes the performance of six models (3 each) of two popular deep learning architectures, Convolutional Neural Networks (CNN) and Vision Transformers (ViT) in the presence of AWGN. AWGN is first introduced to the clean image datasets to conduct the analysis. The CNN and ViT models are trained on the noisy datasets to evaluate the performance of the image classification task. The work also involves six denoising algorithms and a popular image enhancement algorithm- Contrast Limited Adaptive Histogram Equalization (CLAHE).

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Exploring Jaw Morphology and Patient-specific Dynamic Rotation Trajectory Using Cone Beam Computed Tomography

Exploring Jaw Morphology and Patient-specific Dynamic Rotati...

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International Conference on communication Engineering and Technology (ICCET)

作者： P S Anusree Bikram Keshari Parida Wonsang You Dept. of Information and Communication Engineering Artificial Intelligence and Image Processing Laboratory (AIIP Lab) Sun Moon University Asan South Korea

ISBN: (数字)9798350373271

ISBN: (纸本)9798350373288

Cone Beam Computed Tomography (CBCT) is a widely adopted technology in dental healthcare, particularly for surgical planning. However, effective visualization methods are crucial for formatting and analyzing volumetric data. Two primary formatting options - Curved Multi-planar Reformatting and Direct Volume Rendering - are used widely for evaluating jaw shape and creating panoramic views. Existing software methods frequently require the manual selection of several nodes along the jaw on an axial image that roughly represents the shape of the dental arch. Our work intends to introduce a novel and automatic algorithm for detecting the patient’s jaw from CBCT, suitable for defining a focal trough and evaluating an optimal patient-specific dynamic rotation trajectory applicable to rotational panoramic radiography based only on the CBCT volume data without the need to delineate a dental arch. Our findings showed that this approach works well even for patients without teeth or having heavy metal implants.

关键词： Shape Computed tomography Heuristic algorithms Software algorithms Surgery Teeth Rendering (computer graphics)

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Enhanced Focal Liver Lesion Classification using Channel Attention Technique

Enhanced Focal Liver Lesion Classification using Channel Att...

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Conference on Information and communication Technology (CICT)

作者： Sunkanaboina Chandra Lingamaiah Thunakala Bala Krishna Ajay Kumar Reddy Poreddy Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798331531539

ISBN: (纸本)9798331531546

Accurate and timely diagnosis of liver disorders such as fatty liver disease, chronic viral hepatitis, and excessive alcohol consumption is crucial for maintaining liver health. Traditional methods for liver screening are often subjective, time-consuming, and reliant on the expertise of the sonographer, which can impact diagnostic accuracy. To tackle these challenges, in this paper proposed a deep learning (DL) based framework to enhance the effective diagnosis of focal liver lesions. This approach leverages an channel attention mechanism integrated with the DarkNet-19 pre-trained model to improve feature extraction and boosts classification accuracy. By automating the diagnostic process, the proposed model addresses the limitations of traditional methods, providing a more efficient and reliable solution for liver disorder diagnosis. Experimental results with an ultrasound image dataset demonstrate that the proposed model significantly outperforms conventional DL methods, showcasing its advantages in performance and efficiency.

关键词： Frequency locked loops Deep learning Accuracy Ultrasonic imaging Attention mechanisms Liver diseases Feature extraction Information and communication technology Lesions Reliability

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