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Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu 2016年第4期 78-83页

作者： Guo, Hui He, Jie Hu, Defa Jiang, Weijin Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Wuzhou University Wuzhou Guangxi China School of Computer and Information Engineering Hunan University of Commerce Changsha Hunan China

Purpose. Since fractal image coding is time-consuming and is prone to causing "blocking artifact", the article aims to combine fractal image coding, wavelet transform and compressed sensing to put forward a method which can shorten the coding time effectively and improve the quality of a reconstructed image. Methodology. The compressed sensing algorithm can quickly compress and highly restore sparse matrixes. The paper made use of this feature to conduct fractal coding for a low-frequency sub-image after wavelet transform, followed by recoding the samples of low-frequency differential sub-graphs and high-frequency sub-images by means of the compressed sensing algorithm for the purpose of compensating the quality of reconstructed images. Findings. Compared to the traditional fractal coding method, the algorithm in the paper (hereinafter referred to as "this Algorithm") can shorten the time considerably and get a maximum speed-up ratio by up to 6.45 times. Compared to the compressed sensing coding method, the quality of the reconstructed images is improved significantly. Originality. The innovation of the paper lies in applying the compressed sensing theory to the fractal coding algorithm to compensate the quality of the reconstructed images obtained by means of fractal coding based on wavelet transform. Practical value. This Algorithm can shorten the coding time on the basis of ensuring the quality of a reconstructed image, and has certain significance for promoting the fractal coding method. © Hui Guo, Jie He, Defa Hu, Weijin Jiang, 2016.

关键词： Wavelet transforms

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual lab.ling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise lab.l Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function lab.ls are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific lab.ls, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function lab.ls at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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Data-consistent Unsupervised Diffusion Model for Metal Artifact Reduction

Data-consistent Unsupervised Diffusion Model for Metal Artif...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tong, Zhan Wu, Zhan Yang, Yang Mao, Weilong Wang, Shijie Li, Yinsheng Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Chinese Academy of Sciences Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Shenzhen518055 China School of Computer Science and Engineering Key Lab. of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology Nanjing210096 China

ISBN: (纸本)9798350337488

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly availab.e dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics. © 2023 IEEE.

关键词： computed tomography deep unsupervised learning denoising diffusion probabilistic model Metal artifact reduction physics-based consistency constraint

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Geometric Distance for Fast Micro-Expression Detection 20

Geometric Distance for Fast Micro-Expression Detection

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20th IEEE International Conference on Communication Technology, ICCT 2020

作者： Lu, Hua Li, Dengwang Kpalma, Kidiyo Yang, Mingqiang Shandong Normal University Shandong Key Lab. of Med. Physics and Image Processing Shandong Provincial Engineering and Technical Center of Light Manipulations School of Physics and Electronics Jinan250358 China Ietr VAADER- Umr 6164 Univ Rennes Insa Rennes RennesF-35000 France Shandong University School of Information Science and Engineering Qingdao266237 China

ISBN: (纸本)9781728181417

In this paper, we propose a new model for micro-expression detection in videos in conjunction with a set of facial keypoints. The main contribution lies at the construction of geometric features extracted using the geometric distances between pairs of keypoints in different groups. The proposed geometric features-based micro-expression detection model requires a very low computation complexity and simultaneously attains highly accurate detection rates. We compare the proposed geometric features-based model to the existing state-of-the-art micro-expression detection and recognition models in three datasets, where the proposed method has achieved better, or at least comparably accurate results but requiring much less computation time. © 2020 IEEE.

关键词： Support vector machines

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Analysis of multiscale sign series entropy of the young and middle-aged electroencephalogram signals

Analysis of multiscale sign series entropy of the young and ...

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International Congress on image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Fei Du Shitong Wang Jun Wang Jiafei Dai Fengzhen Hou Jin Li Image Processing and Image Communications Key Lab Nanjing Univ. of Posts & Telecomm Nanjing China Nanjing General Hospital of Nanjing Military Command Nanjing China School of Science China Pharmaceutical University Nanjing China College of Physics and Information Technology Shanxi Normal University Xian China

ISBN: (纸本)9781509037117

The physiological analysis of electroencephalogram (EEG) signals is of great significance in assessing the activity of the brain function and the physiological state. EEG is a means of clinical examination of brain diseases. Age is one of the important factors that affect the results of the EEG. EEG signal analysis is mainly to analyze the time series of the signal, multiscale entropy (MSE) analysis [1-3] is the method that used to analyze the finite length of the time series. Multiscale sign series entropy (MSSE) method is proposed for the analysis of EEG signals in the young and middle-aged. We use the proposed method to analyze the signals from several aspects of data length, word length, noise, multi scale etc. By analyzing the influence of these factors, we can still distinguish the EEG signals of different ages. Multiscale sign series entropy (MSSE) analysis algorithm can effectively separate the brain electrical signals from the young and middle aged, which is expected to have a certain reference value for the traditional pathological analysis of the EEG signals.

关键词： Electroencephalography Entropy Time series analysis Brain models Algorithm design and analysis Physiology

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Investigating the stability of fast iterative shrinkage thresholding algorithm for MR imaging reconstruction using compressed sensing

Investigating the stability of fast iterative shrinkage thre...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Guishan Zhang Haitao Deng Yaowen Chen Zhiwei Shen Renhua Wu Key Lab. of Digital Signal and Image Processing of Guangdong Province Shantou University Shantou China Department of Medical Imaging 2nd Affiliated Hospital Medical College of Shantou University Shantou China

ISBN: (纸本)9781467376839

Compressed sensing (CS) has been applied to the field of sub-sampled magnetic resonance imaging (MRI) reconstruction (CS-MRI). Fast iterative shrinkage-thresholding algorithm (FISTA) is an effective method for CS-MR images reconstruction. To investigate the accuracy and efficiency of the proposed algorithm, we applied it to the under-sampling MR images gained by different MRI scanning sequences. We found the peak signal to noise ratio (PSNR) of reconstructed MRI with varying sampling ratios diminished from Axial T1 weighted images (Ax T1) (45.13±12.97 dB), Axial T2 weighted images (Ax T2) (42.8 ± 14.95 dB), FLAIR (41.74 ± 14.15 dB), Diffusion Weighted Imaging (DWI) (40.23 ±17.40 dB) and Sagittal T2 weighted images (Sag T2) (36.28±12.32 dB), but there was no significant difference among the groups. In addition, the changes to the elapsed time of them was minor, Ax T1 (1.09±0.13 s), Ax T2 (1.30±0.13s), FLAIR (1.02±0.12s), DWI (1.07±0.13s) and Sag T2 (1.12±0.07s). Our results confirmed the stability of the proposed fast MRI reconstruction method for different scanning sequences. Further efforts are still needed to design the clinical sequence with sub-sampled acquisition strategy which may be a developing technique with clinical value.

关键词： Compressed Sensing Magnetic Resonance Imaging PSNR Fast Iterative Shrinkage-thresholding Algorithm (FISTA)

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An Approach for National-Scale Accessibility Assessment and Vulnerability Analysis of Integrated Multimodal Transportation Systems

SSRN

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SSRN 2021年

作者： Zhang Ouyang, Min Sun, Wenjing Hong, Liu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Lab. for Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

A series of ambitious accessibility-oriented policies have been launched in the world, which center around improving accessibility of the integrated multimodal transportation systems including road, airline, and high-speed rail. The multimodal accessibility assessment is one of the "basic" analyses for the design and implementation of these policies. This paper models the integrated road, high-speed rail, airline, and walking system (IRHAWS) as a four-layer network with the consideration of their interdependencies. An algorithm is then developed to accurately assess accessibility based on the four-layer network with the consideration of daily schedule of high-speed trains and flights, where the accessibility between two nodes is defined as the minimum travel time given a departure time. The proposed approach enables the time-varied accessibility assessment of both off-network and on-network areas at a national or global scale, and is applied to map the multimodal accessibility to 363 city centers in China and analyze the optimal travel modes for on-network travels. Based on the proposed accessibility assessment approach, this paper also investigates the vulnerability of the Chinese integrated multimodal transportation system under the extreme storm event recently occurred in Zhengzhou (July 2021). The findings in this work provide insightful suggestions to plan the road, high-speed rail, and airline systems as a whole. © 2021, The Authors. All rights reserved.

关键词： Railroad transportation

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An improved algorithm of pattern matching for information security audit system

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Journal of Computational Information Systems 2008年第5期4卷 2221-2228页

作者： Ding, Lei Yu, Fei Xu, Cheng Yue, Guangxue School of Physics Science and Information Engineering Jishou University Jishou 416000 China Provincial Key Lab. of Image Processing and Imagecom Communication Nanjing University of Post and Telecommunications Nanjing 210003 China College of Computer and Communication Hunan University Changsha 410082 China

At present, network information audit system is almost based on text information filtering. Text categorization is the basic technology of text information filtering. Pattern matching algorithms are very important for the rule based information audit system. It directly influences the accuracy and real-time performance of the system. The KMP and BM algorithms are introduced in this paper. Research is carried out to improve the BM algorithm and a better BM algorithm is proposed According to the situation that the system characters group is bigger and that characters of the pat tern are less, the thesis puts forward an improved string-matching algorithm. The results of our experiments prove that it promotes the precision of text categorization. © 2008 Binary Information Press.

关键词： Pattern matching

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A Combining Method for Tumors Detection from Near-infrared Breast Imaging

A Combining Method for Tumors Detection from Near-infrared B...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Zhicheng Wang Jian Liu Jinwen Tian Zeping Xie State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong Univ. of Sci. & Tech. Wuhan 430074 China Key Lab. for Image Process. & Intelligent Control State Educ. Comm. School of Life Science & Technology Huazhong University of Science and Technology Wuhan China

This paper introduces the new qualitative and quantitative methods, which can diagnose breast tumors. Qualitative methods include blood vessel display inside and outside of pathological changes part of breast, display of equivalent pixel curves at the part of pathological changes and display of breast tumor image edge. Accordingly, three feature extraction operators are proposed, i.e. the combination operators of anisotropic gradient and smoothing operator, an improved Sobel operator and an edge sharpening operator. Furthermore, quantitative diagnostic approaches are discussed based on blood and oxygen contents according to abundant clinical data and pathological mechanism of breast tumors. The results of clinic show that the methods of combining qualitative and quantitative diagnose are effective for breast tumor images, especially for early and potential breast cancer

关键词： Breast tumors Displays Pathology Blood vessels Biomedical imaging Pixel Feature extraction Anisotropic magnetoresistance Smoothing methods Breast cancer

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