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7th International Conference on Photonics and imaging in Biology and Medicine

作者： Yangguang, Sun Tian, Lan Xiaowei, Fu Mingyue, Ding Institute of Pattern Recognition and Artificial Intelligence Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China School of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819475398

In this paper we proposed a novel path integral method using multilevel Metropolis sampling to extract the contours of interested objects of medical images, which is a quantum statistical approach inspired by the essential characteristics of quantum. The implemented multilevel Metropolis sampling is a fast approach to the sampling problem and can be used to sample any distribution function and calculate all actions for the movements of quantum particle towards the position with high probability density likely to be accepted. Comparing with traditional deformable models based on classical mechanics which are sensitive to the initialization of contour and difficult to adapt topology changes of the contour, our work has the capability to break through the limitations of deformable models and makes the boundary extraction in high noise and low contrast of medical images possible. Experiments were performed with synthetic and medical images and the feasibility and potential of our method was demonstrated. © 2009 SPIE.

关键词： Distribution functions

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A statistical approach to contour extraction based on quantum mechanics

A statistical approach to contour extraction based on quantu...

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Medical imaging 2009 - Image processing

作者： Sun, Yangguang Lan, Tian Fu, Xiaowei Ding, Mingyue Institute of Pattern Recognition and Artificial Intelligence Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China School of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819475107

Contour extraction is a key issue in many medical applications. A novel statistical approach based on quantum mechanics to extract contour of the interested object of medical images was proposed in this paper. The natures of quantum statistical concepts such as the quantum discontinuity and the wave function correspond to the discrete and gray possibility of an image respectively. Contour extraction will be performed by the quantum particle movement, where the particle will be moved forward to the position with high probability density edges in image potential field. Experimental results with medical images demonstrated that the proposed approach has the capability to extract contours with arbitrary initialization and handle topology changes as well as both the inner and outer contours by a single initialization. keywords: Statistical approach, contour extraction, path integral, quantum mechanics. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Medical applications

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Multi-scale convolutional neural networks for brain disease diagnosis

Multi-scale convolutional neural networks for brain disease ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hu, Zhongyi Gao, Libin Tong, Yinsheng Lu, Xingjin Li, Rui Xiao, Lei Li, Zuoyong Wenzhou University College of Computer Science and Aritificial Intelligence Wenzhou325035 China Key Laboratory of Intelligent Image Processing and Analysis Wenzhou China Minjiang University College of Computer and Control Engineering Fuzhou China

ISBN: (纸本)9798350337488

The brain is the most sophisticated and complex organ in the human body. Nowadays, diagnosing complex and diverse brain diseases is a hot topic. Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), and others are common brain diseases. With the increased usage of Artificial intelligence (AI) in medical image analysis, the endeavor to make AI comprehend brain images for assisting doctors in making objective diagnoses of brain diseases has gained considerable attention. Resting-state functional magnetic resonance imaging (rs-fMRI) is a widely used tool for diagnosing and analyzing brain ***, the Pearson correlation coefficient (PCC) method constructs a dynamic functional connectivity (dFC) network using a fixed window size. However, this method has limitations as it is challenging to extract potential high-level features from the dFC and determine local feature relevance. This paper presents a method for constructing dFC based on multi-scale sliding windows and proposes Multi-scale Convolutional Neural Networks (MsCNN) for learning and analyzing dFC at various scales. Finally, a deep fusion of features learned at different scales is employed for the diagnostic classification of brain *** to the rs-fMRI dataset from ADNI, the classification accuracy was 84.0% for eMCI/lMCI, 84.4% for lMCI/AD, and 58.4% for NC/eMCI/lMCI/AD. Applied to rs-fMRI data from ABIDE, the accuracy was 74.7% for ASD/NC. The proposed method exhibits robust classification performance and introduces a new approach to diagnosing other brain diseases. © 2023 IEEE.

关键词： Brain diseases Classification Diagnostic Multi-scale convolutional neural networks (MsCNN) Resting-state functional magnetic resonance imaging (rs-fMRI)

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Optimal Power and Gas Flow of Energy Internet with Non-Isothermal Process and Energy Storage 43

Optimal Power and Gas Flow of Energy Internet with Non-Isoth...

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43rd Chinese control Conference, CCC 2024

作者： Liu, Chen He, Shiyuan Wang, Xinyi Liu, Xiao-Kang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology Ministry of Education Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China

ISBN: (纸本)9789887581581

The electrical-gas coupled power flow problem is one of the most basic problems under Energy Internet. In order to achieve a more complete analysis of the electric-gas coupled power flow, this paper establishes a novel integrated energy system model that considers electricity and natural gas. This model integrates the power system AC power flow model and the non-isothermal process of natural gas pipeline transmission, achieving a more accurate description of the power grid and gas network power flow. At the same time, the beneficial effects of energy storage units on the network of each part of the system are analyzed. Through simulation design, the benefits of energy storage access on system cost and security are analyzed and quantified. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Energy storage

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Fog Images Generation About Unmanned Surface Vessels with Improved Generative Adversarial Network 42

Fog Images Generation About Unmanned Surface Vessels with Im...

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42nd Chinese control Conference, CCC 2023

作者： Tang, Yifeng Huang, Zhihui Tang, Chuancong Luo, Chuanshang Xu, Yifan Liu, Bin Zhang, Hai-Tao School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789887581543

This paper proposes a fog weather data augmentation method for the unmanned surface vessels (USVs) via improved Generative Adversarial Network(GAN) model. First, a generator scheme for GAN is proposed with the guided generation of the atmospheric scattering model in this paper. A Laplacian Pyramid Based Depth Residuals model is added to the generator which reduces the difficulty of generating fog images caused by the degradation of water surface image and improves the quality of generated images. Finally, fog images are generated from sunny weather images collected with HUST-12C by LPBDR-GAN model and experiments show that generated images are very close to real fog images. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Generative adversarial networks

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Cross-Modality Comprehensive Feature Aggregation Network for Road Scene Segmentation 8

Cross-Modality Comprehensive Feature Aggregation Network for...

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8th International Symposium on Computer Science and Intelligent control, ISCSIC 2024

作者： Yang, Yale Zhang, Zihao Hou, Huifang Zhang, Wenwen Zhuang, Chunsheng Henan University of Technology College of Information Science and Engineering Zhengzhou China Henan University of Technology Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control School of Artificial Intelligence and Big Data Zhengzhou China Data Henan University of Technology School of Artificial Intelligence and Big Zhengzhou China Henan Academy of Sciences Zhengzhou China

ISBN: (纸本)9798350380286

RGB-thermal semantic segmentation is a potential solution for reliable semantic scene understanding under adverse weather and complex lighting conditions. However, past studies have primarily focused on the design of fusion paradigms. Unfortunately, these methods have not fully considered the modality differences caused by different imaging mechanisms, thereby reducing the performance of pixel-level semantic analysis. In response to this issue, this paper proposes an innovative Cross-Modality Comprehensive Feature Aggregation Network (CFANet). This method first designs a Cross-Modality Feature Rectification Module (CFRM) to align modality differences;then, it constructs a Global-Local Fusion Module (GLF) to sharpen object edges and refine the segmentation map. Results on two urban scene understanding datasets demonstrate that CFANet can effectively aggregate multi-layer deep features and outperform existing advanced deep learning methods in scene understanding performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Feature Selection Based on Adaptive Fuzzy Membership Functions

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自动化学报 2006年第4期32卷 496-503页

作者： XIE Yan-Tao SANG Nong ZHANG Tian-Xu Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074

Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some ***,their rule number will grow exponentially as the data dimension *** the other hand,feature selection algorithms with artificial neural networks(ANN)usually require normalization of input data,which will probably change some characteristics of original data that are important for *** overcome the problems mentioned above,this paper combines the fuzzification layer of the neuro-fuzzy system with the multi-layer perceptron(MLP)to form a new artificial neural ***,fuzzification strategy and feature measurement based on membership space are proposed for feature selection. Finally,experiments with both natural and artificial data are carried out to compare with other methods,and the results approve the validity of the algorithm.

关键词： Membership function feature selection architecture pruning artificial neural networks

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Research on attention mechanism based assisted diagnosis of pulmonary embolism 1

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19th Chinese Intelligent Systems Conference, CISC 2023

作者： Li, Hua Tao Hu, Zhong Yi Hu, Ming Zhe College of Computer and Artificial Intelligence WenZhou University Wenzhou325035 China WenZhou Intelligent Information Processing and Key Analysis Laboratory Wenzhou325035 China Radiology and Imaging Department of WenZhou People’s Hospital WenZhou China

ISBN: (数字)9789819968824

ISBN: (纸本)9789819968817

The traditional diagnosis of pulmonary embolism (PE) requires doctors to distinguish between computed tomography (CT) images, which is a very timeconsuming task and may result in patients not receiving timely and effective treatment. Therefore, the use of computer intelligence to assist in diagnosis is crucial. In this paper, we propose a Coordination and spatial attention convNext (CSACNet) based on CSA attention mechanism to realize early intelligent auxiliary diagnosis of pulmonary embolism disease. First, Convolutional neural network uses ConvNext network as the backbone network for feature extraction, which can solve the gradient problem caused by network deepening. Secondly, the CSA attention module introduced is a fusion of CoordAttention and Spatial Attention, extracting channel, position, and spatial information between features to obtain more discriminative features and improve the network’s classification accuracy for pulmonary embolism images. The proposed method was tested on the largest publicly contested PE dataset(RSNASTR), and the experimental results showed that it outperformed the current best method and improved the performance of pulmonary embolism assisted diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Diseases

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Estimation of Motor Imagination Based on Consumer-Grade EEG Device 3rd

Estimation of Motor Imagination Based on Consumer-Grade EEG ...

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3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Luo, Zhenzhen Hu, Zhongyi Li, Zuoyong College of Computer and Artificial Intelligence Wenzhou University Wenzhou China Intelligent Information Systems Institute Wenzhou University Wenzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (纸本)9783030624590

Nowadays, classifying electroencephalogram (EEG) signals based motor imagery tasks is extensively used to control brain-computer interface applications, as a communication bridge between humans and computers. In this paper, we propose signal-to-image transformation and feature extraction methods for the classification of motor imagery. Specifically, a continuous wavelet transform is applied to decompose EEG signals into five rhythms and generate time-frequency images. Then, a gray-level co-occurrence matrix is used to extract global texture features on time-frequency images. Finally, the SVM classification model is optimized by using a grid search algorithm to select optimal parameter pairs. To confirm the validity of the proposed methods, we experimented on self-collected data, which is obtained using a consumer-grade EEG device. The experimental result showed that this proposed method can achieve an acceptable classification accuracy of 90% for a two-class problem (left/right-hand motor imagery). © 2020, Springer Nature Switzerland AG.

关键词： Electroencephalography

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A framework for spatial-variant thresholding of digital subtraction angiography images

A framework for spatial-variant thresholding of digital subt...

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MIPPR 2005: Geospatial Information, Data Mining, and Applications

作者： Sang, Nong Peng, Weixue Li, Heng Zhang, Tianxu Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

Vessel segmentation is the base of 3d reconstruction of Digital Subtraction Angiograph (DSA) images. This paper proposes a framework of adaptive local thresholding based on a verification-based approach for vessel segmentation of DSA images. The original DSA image is firstly divided into overlapping subimages according to a priori knowledge of the diameter of vessels. We implement a hypothesis test to determine whether each subimage contains vessels and then choose an optimal threshold respectively for every subimage previously determined to contain vessels, with a secondary verification process to exclude the condition that the subregion only containing the background but misclassified as one containing vessels by the hypothesis test. Finally an overall binarization of the original image is achieved by combining the thresholded subimages. Experiments demonstrate superior performance over global thresholding and some adaptive local thresholding methods.

关键词： Angiography

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