Article
Computer Science, Artificial Intelligence
Zeyu Wang, Xiongfei Li, Haoran Duan, Yanchi Su, Xiaoli Zhang, Xinjiang Guan
Summary: A novel multimodal medical image fusion method based on NSCT and CNN is proposed in this paper, which not only solves the problem of CNN inability to be directly used in medical image fusion, but also demonstrates its effectiveness in fusing multimodal medical images through subjective and objective evaluations.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Physics, Multidisciplinary
Bingzhe Wei, Xiangchu Feng, Kun Wang, Bian Gao
Summary: A novel fusion method that combines CNN and SR for multi-focus image fusion has been proposed, resulting in a more accurate and informative fused image. Experimental results demonstrate that this method clearly outperforms existing methods in terms of visual perception and objective evaluation metrics, while also significantly reducing computational complexity.
Article
Computer Science, Software Engineering
Chengfang Zhang
Summary: In this paper, a fusion method based on MST and CSR is proposed to address the inherent defects of traditional fusion methods. Experimental results show that the algorithm exhibits state-of-the-art performance in terms of definition.
INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING
(2021)
Article
Engineering, Electrical & Electronic
D. Sunderlin Shibu, S. Suja Priyadharsini
Summary: The proposed method combines low frequency layers of different modal images to preserve detail and improve quality, while combining high frequency layers to maintain curve edges and image energy. Experimental results show that this method outperforms current methodologies in terms of visual consistency and quantitative analysis.
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
(2021)
Article
Physics, Multidisciplinary
Yuchan Jie, Xiaosong Li, Mingyi Wang, Haishu Tan
Summary: Full-field optical angiography (FFOA) has the potential for clinical applications in disease prevention and diagnosis. However, existing FFOA imaging techniques can only acquire blood flow information within a limited depth of focus, resulting in partially unclear images. To address this issue, a novel FFOA image fusion method based on the nonsubsampled contourlet transform and contrast spatial frequency is proposed, which significantly expands the range of focus and outperforms state-of-the-art methods.
Article
Engineering, Electrical & Electronic
Kai Zhang, Feng Zhang, Zhixi Feng, Jiande Sun, Quanyuan Wu
Summary: A novel image fusion method based on multiscale convolution sparse decomposition (MCSD) is proposed in this article, which efficiently approximates the spatial and spectral information in images. By decomposing and integrating the components of panchromatic and multispectral images, the method performs better in visual and numerical evaluations compared to other methods.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Computer Science, Artificial Intelligence
Xiaosong Li, Fuqiang Zhou, Haishu Tan
Summary: The proposed image fusion method is based on three-layer decomposition and sparse representation, which effectively fuses and denoises high-frequency components by adaptively designing sparse reconstruct error parameters. Additionally, the structure-texture decomposition model and carefully designed fusion rules are used to fully utilize details and energy in low-frequency components. The experimental results show that this method can effectively address clean and noisy image fusion problems and outperform some state-of-the-art methods in subjective visual and quantitative evaluations.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Engineering, Biomedical
Ahmed Sabeeh Yousif, Zaid Omar, Usman Ullah Sheikh
Summary: Multimodal image fusion is a contemporary branch of medical imaging that aims to improve the quality of medical images and enhance the accuracy of clinical diagnosis by combining sparse representation and Siamese Convolutional Neural Network methods. This approach effectively addresses the defects of traditional models and significantly improves the overall fused image quality.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Engineering, Electrical & Electronic
Tianlin Liu, Anadi Chaman, David Belius, Ivan Dokmanic
Summary: The study explores the success of convolutional neural networks in imaging inverse problems and proposes a multiscale convolutional dictionary structure that can compete with state-of-the-art CNNs and perform well on a range of challenging inverse problems.
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
(2022)
Article
Chemistry, Analytical
Wenfeng Zheng, Bo Yang, Ye Xiao, Jiawei Tian, Shan Liu, Lirong Yin
Summary: This paper introduces an algorithm to improve low-dose CT images using sparse representation and image decomposition theory. Experimental results demonstrate the effectiveness of the algorithm.
Article
Chemistry, Analytical
Nagaraj Yamanakkanavar, Jae Young Choi, Bumshik Lee
Summary: In this paper, we propose an encoder-decoder architecture with wide and deep convolutional layers combined with different aggregation modules for medical image segmentation. The method achieves a rich representation of features spanning from low to high levels and from small to large scales using stacked kernels, and introduces feature-aggregation modules to better fuse information across network layers. The proposed method improves segmentation accuracy by combining feature-aggregation modules with guided skip connections.
Article
Environmental Sciences
Chunhui Zhao, Boao Qin, Shou Feng, Wenxiang Zhu
Summary: In this paper, a novel multiple superpixel graphs learning method based on adaptive multiscale segmentation (MSGLAMS) is proposed for hyperspectral image classification. The method addresses the challenge of insufficient accuracy and stability under the condition of small labeled training sample size (SLTSS). Experimental results demonstrate that MSGLAMS outperforms other state-of-the-art algorithms.
Article
Computer Science, Information Systems
Pengcheng Hu, Shihua Tang, Yan Zhang, Xiaohui Song, Mengbo Sun
Summary: Existing image processing methods usually separate image denoising and image fusion for research, but the best current image denoising methods may cause information loss. The proposed method combines image denoising and fusion in the NSCT transform domain to reconstruct remote sensing images, preserving texture details, highlighting edge contour structures, and enriching image energy.
Article
Instruments & Instrumentation
Jianming Zhang, Wenxin Lei, Shuyang Li, Zongping Li, Xudong Li
Summary: This paper proposes a novel algorithm for infrared and visible image fusion. The algorithm decomposes the input image into different layers using a guided filter, and then fuses them using entropy-based fusion module, maximum absolute value rule, and a mask-guided deep convolutional neural network. Experimental results demonstrate that the algorithm achieves good performance in both subjective and objective evaluation.
INFRARED PHYSICS & TECHNOLOGY
(2023)
Article
Computer Science, Software Engineering
Peng Hu, Chenjun Wang, Dequan Li, Xin Zhao
Summary: An improved hybrid multiscale fusion algorithm inspired by NSST is proposed to effectively extract and fuse complementary information in infrared-visible images, resulting in improved image contrast and retained saliency details.
Article
Engineering, Biomedical
Wenwen Wu, Yanqi Huang, Xiaomei Wu
Summary: In this study, a 2D deep learning classification network SRT was proposed to improve automatic ECG analysis. The model structure was enhanced with the CNN and Transformer-encoder modules, and a novel attention module and Dilated Stem structure were introduced to improve feature extraction. Comparative experiments showed that the proposed model outperformed several advanced methods.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Chiheb Jamazi, Ghaith Manita, Amit Chhabra, Houssem Manita, Ouajdi Korbaa
Summary: In this study, a new dynamic and intelligent clustering method for brain tumor segmentation is proposed by combining the improved Aquila Optimizer (AO) and the K-Means algorithm. The proposed MAO-Kmeans approach aims to automatically extract the correct number and location of cluster centers and the number of pixels in each cluster in abnormal MRI images, and the experimental results demonstrate its effectiveness in improving the performance of conventional K-means clustering.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Alberto Hernando, Maria Dolores Pelaez-Coca, Eduardo Gil
Summary: This study applied a new algorithm to decompose the photoplethysmogram (PPG) pulse and identified changes in PPG pulse morphology due to pressure. The results showed that there was an increase in amplitude, width, and area values of the PPG pulse, and a decrease in ratios when pressure increased, indicating vasoconstriction. Furthermore, some parameters were found to be related to the pulse-to-pulse interval.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Jens Moeller, Eveline Popanda, Nuri H. Aydin, Hubert Welp, Iris Tischoff, Carsten Brenner, Kirsten Schmieder, Martin R. Hofmann, Dorothea Miller
Summary: In this study, a method based on texture features is proposed, which can classify healthy gray and white matter against glioma degrees 4 samples with reasonable classification performance using a relatively low number of samples for training. The method achieves high classification performance without the need for large datasets and complex machine learning approaches.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Amrutha Bhaskaran, Manish Arora
Summary: The study evaluates a cyclic repetition frequency-based algorithm for fetal heart rate estimation. The algorithm improves accuracy and reliability for poor-quality signals and performs well for different gestation weeks and clinical settings.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Manan Patel, Harsh Bhatt, Manushi Munshi, Shivani Pandya, Swati Jain, Priyank Thakkar, Sangwon Yoon
Summary: Electroencephalogram (EEG) signals have been effectively used to measure and analyze neurological data and brain-related ailments. Artificial Intelligence (AI) algorithms, specifically the proposed CNN-FEBAC framework, show promising results in studying the EEG signals of autistic patients and predicting their response to stimuli with 91% accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Wencheng Gu, Kexue Sun
Summary: This research proposes an improved version of YOLOv5 (AYOLOv5) based on the attention mechanism to address the issue of low recognition rate in cell detection. Experimental results demonstrate that AYOLOv5 can accurately identify cell targets and improve the quality and recognition performance of cell pictures.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Anita Gade, V. Vijaya Baskar, John Panneerselvam
Summary: Analysis of exhaled breath is an increasingly used diagnostic technique in medicine. This study introduces a new NICBGM-based model that utilizes various features and weight optimization for accurate data interpretation and result optimization.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Arsalan Asemi, Keivan Maghooli, Fereidoun Nowshiravan Rahatabad, Hamid Azadeh
Summary: Biometric authentication systems can perform identity verification with optimal accuracy in various environments and emotional changes, while the performance of signature verification systems can be affected when people are under stress. This study examines the performance of a signature verification system based on muscle synergy patterns as biometric characteristics for stressed individuals. EMG signals from hand and arm muscles were recorded and muscle synergies were extracted using Non-Negative Matrix Factorization. The extracted patterns were classified using Support Vector Machine for authentication of stressed individuals.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tianjiao Guo, Jie Yang, Qi Yu
Summary: This paper proposes a CNN-based approach for segmenting four typical DR lesions simultaneously, achieving competitive performance. This approach is significant for DR lesion segmentation and has potential in other segmentation tasks.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
G. Akilandasowmya, G. Nirmaladevi, S. U. Suganthi, A. Aishwariya
Summary: This study proposes a technique for skin cancer detection and classification using deep hidden features and ensemble classifiers. By optimizing features to reduce data dimensionality and combining ensemble classifiers, the proposed method outperforms in skin cancer classification and improves prediction accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tuuli Uudeberg, Juri Belikov, Laura Paeske, Hiie Hinrikus, Innar Liiv, Maie Bachmann
Summary: This article introduces a novel feature extraction method, the in-phase matrix profile (pMP), specifically adapted for electroencephalographic (EEG) signals, for detecting major depressive disorder (MDD). The results show that pMP outperforms Higuchi's fractal dimension (HFD) in detecting MDD, making it a promising method for future studies and potential clinical use for diagnosing MDD.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
P. Nancy, M. Parameswari, J. Sathya Priya
Summary: Stroke is the third leading cause of mortality worldwide, and early detection is crucial to avoid health risks. Existing research on disease detection using machine learning techniques has limitations, so a new stroke detection system is proposed. The experimental results show that the proposed method achieves a high accuracy rate in stroke detection.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Shimin Liu, Zhiwen Huang, Jianmin Zhu, Baolin Liu, Panyu Zhou
Summary: In this study, a continuous blood pressure (BP) monitoring method based on random forest feature selection (RFFS) and a gray wolf optimization-gradient boosting regression tree (GWO-GBRT) prediction model was developed. The method extracted features from electrocardiogram (ECG) and photoplethysmography (PPG) signals, and employed RFFS to select sensitive features highly correlated with BP. A hybrid prediction model of gray wolf optimization (GWO) technique and gradient boosting regression tree (GBRT) algorithm was established to learn the relationship between BP and sensitive features. Experimental results demonstrated the effectiveness and advancement of the proposed method.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Weijun Gong, Yurong Qian, Weihang Zhou, Hongyong Leng
Summary: The recognition of dynamic facial expressions is challenging due to various factors, and obtaining discriminative expression features has been difficult. Traditional deep learning networks lack understanding of global and temporal expressions. This study proposes an enhanced spatial-temporal learning network to improve dynamic facial expression recognition.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)