Review
Biology
Muhammad Adeel Azam, Khan Bahadar Khan, Sana Salahuddin, Eid Rehman, Sajid Ali Khan, Muhammad Attique Khan, Seifedine Kadry, Amir H. Gandomi
Summary: This article provides a comprehensive overview of multimodal medical image fusion methodologies, databases, and quality measurements. Medical imaging modalities are categorized based on radiation, visible-light imaging, microscopy, and multimodal imaging. Fusion techniques are classified into categories including frequency fusion, spatial fusion, decision-level fusion, deep learning, hybrid fusion, and sparse representation fusion. The associated diseases for each modality and fusion approach are presented, and quality assessment fusion metrics are also discussed.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Review
Computer Science, Information Systems
Sajid Ullah Khan, Mir Ahmad Khan, Muhammad Azhar, Faheem Khan, Youngmoon Lee, Muhammad Javed
Summary: Medical imaging has been widely used in diagnosing various disorders, but the challenge lies in accurate disease identification and improved therapies. Multi modal image fusion (MMIF) aims to combine complementary information from different imaging modalities to improve the quality and clear assessment of medical related problems. This review provides a detailed overview of medical imaging modalities, multimodal medical image databases, MMIF steps/rules, methods, performance evaluation, and future directions. It is expected to be valuable in developing more effective medical image fusion methods for clinical diagnosis.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Marwah Mohammad Almasri, Abrar Mohammed Alajlan
Summary: In this paper, a multimodal medical image fusion-based artificial intelligence model is proposed, which utilizes a modified discrete wavelet transform to obtain multimodal medical images and a hybrid optimization dynamic algorithm for image fusion and malignant/benign classification. The experimental results demonstrate that the proposed approach outperforms other methods and provides high-quality fused images for accurate diagnosis.
Article
Optics
Shaik Shehanaz, Ebenezer Daniel, Sitaramanjaneya Reddy Guntur, Sivaji Satrasupalli
Summary: Medical image fusion technique using optimum weighted average fusion (OWAF) improves multimodal mapping performance by decomposing input modalities with discrete wavelet transform (DWT) and weighting energy bands using particle swarm optimization algorithm (PSO). The proposed approach outperforms existing methods in terms of information mapping, edge quality and structural similarity in MR/PET, MR/SPECT and MR/CT images under normal and noisy fusion backgrounds.
Review
Computer Science, Information Systems
Shatabdi Basu, Sunita Singhal, Dilbag Singh
Summary: Medical image fusion is a relevant field that has wide applications in disease diagnosis and prediction. It aims to combine multiple images of the same or different modality to enhance the image content and provide more information about diseases using easily available image scans. Multimodal medical image fusion can improve the quality and accuracy of medical images for diagnosis and treatment planning.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Nahed Tawfik, Heba A. Elnemr, Mahmoud Fakhr, Moawad I. Dessouky, Fathi E. Abd El-Samie
Summary: This paper presents a hybrid algorithm for multimodal medical image fusion, incorporating both pixel and feature-level fusion methods. Experimental results demonstrate that the proposed method improves the quality of the final fused image in various aspects, such as Mutual Information, Correlation Coefficient, entropy, Structural Similarity Index, Peak Signal-to-Noise Ratio, and edge-based similarity measure.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
Review
Computer Science, Information Systems
B. Venkatesan, U. S. Ragupathy, Indhu Natarajan
Summary: Image fusion is a technique that merges multiple images into a single image, providing more accurate and detailed information. It has been applied in various fields and has moved from the laboratory to real-time applications. Ongoing research aims to develop better fusion techniques for future applications.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
B. Venkatesan, U. S. Ragupathy
Summary: Nowadays, medical image fusion plays an important role in clinical diagnosis, allowing for the integration of multiple imaging modalities to obtain more accurate and efficient diagnostic features. In this study, a hybrid algorithm combining Discrete Wavelet Transform (DWT) and Deep Neural Network is proposed to fuse CT and MR brain images, with a focus on healthcare. Evaluation metrics demonstrate that the proposed fusion method outperforms traditional wavelet transform-based fusion, with 5.58% and 26.74% improvements in average entropy and standard deviation, respectively.
MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Biochemical Research Methods
Rui Zhu, Xiongfei Li, Sa Huang, Xiaoli Zhang
Summary: In this article, a novel medical image fusion method is proposed, utilizing skewness of pixel intensity and an adaptive co-occurrence filter for image decomposition optimization. Experimental results show that this method outperforms 22 state-of-the-art methods in terms of quality and computational efficiency.
Article
Computer Science, Information Systems
Weiwei Kong, Qiguang Miao, Ruyi Liu, Yang Lei, Jing Cui, Qiang Xie
Summary: Multimodal imaging technology plays an important role in clinical diagnosis and treatment planning. This paper presents a novel medical image fusion method, which combines gradient domain-guided filter random walk (GDGFRW) and side window filtering (SWF) in the framelet transform (FT) domain. The proposed method outperforms current representative methods in terms of subjective visual performance and objective assessment.
INFORMATION SCIENCES
(2022)
Article
Engineering, Electrical & Electronic
Aswini K. Samantaray, Pranose J. Edavoor, Amol D. Rahulkar
Summary: This paper presents a novel design of a family of multiplier-free orthogonal wavelet filter banks (FBs) with low complexity. The proposed method ensures maximum number of zeros at z = -1 for a desired length of analysis and synthesis filters. A new technique is proposed to obtain dyadic filter coefficients based on double shifting orthogonality property. The designed wavelet FB improves time-frequency product, Sobolev regularity and frequency selectivity over existing wavelet FBs. In addition, a fixed point VLSI architecture is proposed for the designed family of orthogonal wavelet FBs.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Review
Medicine, General & Internal
Manoj Diwakar, Prabhishek Singh, Vinayakumar Ravi, Ankur Maurya
Summary: Today, medical images are crucial for obtaining relevant clinical information, but their quality needs analysis and improvement. Various factors affect the quality of medical images, and multi-modality image fusion is beneficial for obtaining clinically relevant information. However, there are numerous techniques for multi-modality image fusion in the literature, each with its own assumptions, merits, and barriers. This paper critically analyzes non-conventional methods of multi-modality image fusion, providing assistance for researchers in understanding and choosing appropriate approaches.
Article
Engineering, Electrical & Electronic
Chengchao Wang, Rencan Nie, Jinde Cao, Xue Wang, Ying Zhang
Summary: Multimodal medical image fusion is a technique that aims to merge saliency and complementary information from different source images to assist in biomedical diagnoses. In this paper, the authors propose a new information gate network (IGNFusion) and a Siamese multi-scale cross attention fusion module (SMSCAFM) to optimize the fusion process, achieving significant improvements over existing methods on multiple datasets.
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
(2022)
Article
Computer Science, Information Systems
Muhammad Adeel Azam, Khan Bahadar Khan, Muhammad Ahmad, Manuel Mazzara
Summary: Over the past two decades, the use of a multimodal approach in medical imaging has proven to be effective in increasing both qualitative and quantitative information for accurate disease diagnosis. The integration of Multi-resolution Rigid Registration technique and Discrete Wavelet Transform with Principal Component Averaging in image fusion has shown to produce more accurate results and valuable medical information in a shorter computational time. The proposed method, tested on CT and MRI brain imaging modalities, offers improved image quality and enhanced medical diagnoses compared to existing techniques.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Nahed Tawfik, Heba A. Elnemr, Mahmoud Fakhr, Moawad I. Dessouky, Fathi E. Abd El-Samie
Summary: Medical image fusion is a process of merging important information from different modality images to create a more informative fused image. Deep learning methods have achieved significant breakthroughs in the field of image fusion. This paper proposes a medical image fusion method based on stacked sparse auto-encoder (SSAE) and non-subsampled contourlet transform (NSCT), which has been evaluated on various medical image modalities.
JOURNAL OF DIGITAL IMAGING
(2022)
Article
Computer Science, Information Systems
Siddharth Singh, Vivek Singh Rathore, Rajiv Singh
MULTIMEDIA TOOLS AND APPLICATIONS
(2017)
Article
Computer Science, Information Systems
Siddharth Singh, Vivek Singh Rathore, Rajiv Singh, Manoj Kumar Singh
MULTIMEDIA TOOLS AND APPLICATIONS
(2017)
Review
Computer Science, Interdisciplinary Applications
Swati Nigam, Rajiv Singh, A. K. Misra
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2019)
Article
Computer Science, Information Systems
Swati Nigam, Rajiv Singh, A. K. Misra
MULTIMEDIA TOOLS AND APPLICATIONS
(2018)
Article
Computer Science, Software Engineering
Chandan Kumar, A. K. Singh, P. Kumar, Rajiv Singh, Siddharth Singh
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2020)
Article
Telecommunications
Ankit Agarwal, Manju Khari, Rajiv Singh
Summary: DDoS attacks have become a serious threat to network security in recent years, leading to a focus on detecting and defending against them. Deep learning techniques have been identified as an effective algorithm for classifying normal and attacked information. A novel FS-WOA-DNN method has been proposed with a 95.35% accuracy in detecting DDoS attacks.
WIRELESS PERSONAL COMMUNICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Shimpy Goyal, Rajiv Singh
Summary: Various types of research have been initiated worldwide to accurately predict the novel Covid-19, with a particular focus on distinguishing it from pneumonia. A proposed framework utilizes image enhancement and deep learning techniques to predict lung diseases, showing robustness and efficiency compared to existing methods.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
Article
Computer Science, Information Systems
Ankit Agrawal, Rajiv Singh, Manju Khari, S. Vimal, Sangsoon Lim
Summary: The study proposes a method using modified deep neural network (M-DBNN) and Chimp Optimization Algorithm (ChOA) to accurately detect DDoS attacks, showing high accuracy and low error compared to other methods.
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
(2022)
Review
Computer Science, Interdisciplinary Applications
Bhawana Tyagi, Swati Nigam, Rajiv Singh
Summary: In today's crowded events, the possibility of suspicious activities and violence increases. Developing automated systems for crowd analysis is important to ensure public safety and prevent disasters. This analysis involves density estimation, crowd counting, object recognition, tracking, and anomaly detection. Comparing different methods and creating taxonomies for crowd analysis are also important for effective implementation.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2022)
Article
Computer Science, Information Systems
Sakshi Indolia, Swati Nigam, Rajiv Singh, Vivek Kumar Singh, Manoj Kumar Singh
Summary: This paper proposes a vision transformer based on convolution patches for the classification of micro-expressions, which combines convolutional layers and transformers to capture both spatial information and global dependencies, leading to improved performance.
Proceedings Paper
Computer Science, Theory & Methods
Shimpy Goel, Rajiv Singh
2019 FIFTH INTERNATIONAL CONFERENCE ON IMAGE INFORMATION PROCESSING (ICIIP 2019)
(2019)
Proceedings Paper
Computer Science, Hardware & Architecture
Siddharth Singh, Rajiv Singh, Tanveer J. Siddiqui
2016 FOURTH INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED AND GRID COMPUTING (PDGC)
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
Siddharth Singh, Rajiv Singh, Tanveer J. Siddiqui
ADVANCES IN SIGNAL PROCESSING AND INTELLIGENT RECOGNITION SYSTEMS (SIRS-2015)
(2016)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Richa Srivastava, Rajiv Singh, Ashish Khare
2013 SIXTH INTERNATIONAL CONFERENCE ON CONTEMPORARY COMPUTING (IC3)
(2013)
Article
Computer Science, Artificial Intelligence
C. Lopez-Molina, S. Iglesias-Rey, B. De Baets
Summary: Quantitative image comparison is a critical topic in image processing literature, with diverse applications. Existing measures of comparison often overlook the context in which the comparison takes place. This paper presents a context-aware comparison method for binary images, tested on the BSDS500 benchmark.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Lorenz Linhardt, Klaus-Robert Mueller, Gregoire Montavon
Summary: This paper investigates the issue of mismatches between the decision strategy of the explainable model and the user's domain knowledge, and proposes a new method EGEM to mitigate hidden flaws in the model. Experimental results demonstrate that the approach can significantly reduce reliance on Clever Hans strategies and improve the accuracy of the model on new data.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Zhimin Shao, Weibei Dou, Yu Pan
Summary: This paper proposes a novel algorithm, Dual-level Deep Evidential Fusion (DDEF), to integrate multimodal information at both the BBA level and multimodal level, aiming to enhance accuracy, robustness, and reliability. The DDEF approach utilizes the Dirichlet framework and BBA methods for effective uncertainty estimation and employs the Dempster-Shafer Theory for dual-level fusion. The experimental results show that the proposed DDEF outperforms existing methods in synthetic digit classification and real-world medical prognosis after BCI treatment.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Abhishek K. Ghosh, Danilo S. Catelli, Samuel Wilson, Niamh C. Nowlan, Ravi Vaidyanathan
Summary: The inability of current FM monitoring methods to be used outside clinical environments has made it challenging to understand the nature and evolution of FM. This investigation introduces a novel wearable FM monitor with a heterogeneous sensor suite and a data fusion architecture to efficiently capture and separate FM from interfering artifacts. The performance of the device and architecture were validated through at-home use, demonstrating high accuracy in detecting and recognizing FM events. This research is a major milestone in the development of low-cost wearable FM monitors for pervasive monitoring of FM in unsupervised environments.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jianlei Kong, Xiaomeng Fan, Min Zuo, Muhammet Deveci, Xuebo Jin, Kaiyang Zhong
Summary: In this study, we propose an intelligent traffic flow prediction framework based on the adaptive dual-graphic transformer with a cross-fusion strategy, aiming to uncover latent graphic feature representations that transcend temporal and spatial limitations. By establishing a traffic spatiotemporal prediction model using a cross-fusion attention mechanism, our proposed model achieves superior prediction performance on practical urban traffic flow datasets, particularly for long-term predictions.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huilai Zhi, Jinhai Li
Summary: This article addresses the issue that conflict analysis based on single-valued information systems is no longer valid. It proposes a conflict analysis method based on component similarity, which uses three-way n-valued concept lattices to handle set-valued formal contexts and realizes fast conflict analysis from an information fusion viewpoint. Experimental results verify the effectiveness of this method in reducing time consumption.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huchang Liao, Jiaxin Qi, Jiawei Zhang, Chonghui Zhang, Fan Liu, Weiping Ding
Summary: In this paper, a hospital selection approach based on a fuzzy multi-criterion decision-making method is proposed. This approach considers sentiment evaluation values of unstructured data from online reviews and structured data of public indexes simultaneously. The methodology involves collecting and processing online reviews, classifying topics and sentiments, quantifying sentiment analysis results using fuzzy numbers, and obtaining final preference scores of hospitals based on patients' preferences. A case study and robustness analysis are conducted to validate the effectiveness of the method.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Faramarz Farhangian, Rafael M. O. Cruz, George D. C. Cavalcanti
Summary: The proliferation of social networks has posed challenges in combating fake news, but automatic fake news detection using artificial intelligence has become more feasible. This paper revisits the definitions and perspectives of fake news and proposes an updated taxonomy, based on multiple criteria, for the field. The study finds that optimal feature extraction techniques vary depending on the dataset, and context-dependent models based on transformer models consistently exhibit superior performance.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti, Rafael M. O. Cruz
Summary: In this study, a dynamic selection technique is proposed to handle sparse and overlapped data. The technique leverages the relationships between instances and classifiers to learn a dynamic classifier combination rule. Experimental results show that the proposed method outperforms static selection and other dynamic selection techniques.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Bin Yu, Ruihui Xu, Mingjie Cai, Weiping Ding
Summary: This paper introduces a clustering method based on non-Euclidean metric and multi-granularity staged clustering to address the challenges posed by complex spatial structure data to traditional clustering methods. The method improves the similarity measure and employs an attenuation-diffusion pattern for local to global clustering, achieving good clustering results.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jian Zhu, Pengbo Hu, Bingqian Li, Yi Zhou
Summary: The acquisition of multi-view hash representation for heterogeneous data is highly important for multimedia retrieval. Current approaches suffer from limited retrieval precision due to insufficient integration of multi-view features and failure to effectively utilize metric information from diverse samples. In this paper, we propose an innovative method called Fast Metric Multi-View Hashing (FMMVH), which demonstrates the superiority of gate-based fusion over traditional methods. We also introduce a novel deep metric loss function to leverage metric information from dissimilar samples. By optimizing and employing model compression techniques, our FMMVH method significantly outperforms existing state-of-the-art methods on benchmark datasets, with up to 7.47% improvement in mean Average Precision (mAP).
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Fayaz Ali Dharejo, Iyyakutti Iyappan Ganapathi, Muhammad Zawish, Basit Alawode, Moath Alathbah, Naoufel Werghi, Sajid Javed
Summary: The resource-limited nature of underwater vision equipment affects underwater robotics and ocean engineering tasks. Super-resolution methods, particularly using Vision Transformers (ViTs), have emerged to enhance low-resolution underwater images. However, ViTs face challenges in handling severe degradation in underwater imaging. In contrast, Multi-scale ViTs (MViTs) overcome these challenges by preserving long-range dependencies through evolving channel capacity. This study proposes a novel algorithm, SwinWave-SR, for efficient and accurate multi-scale super-resolution for underwater images.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Weiwei Jiang, Haoyu Han, Yang Zhang, Jianbin Mu
Summary: This study incorporates federated learning and split learning paradigms with satellite-terrestrial integrated networks and introduces a split-then-federated learning framework and federated split learning with long short-term memory to handle sequential data in STINs. The proposed solution is demonstrated to be effective through a case study of electricity theft detection based on a real-world dataset.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Najah Abuali, Mohammad Bilal Khan, Farman Ullah, Mohammad Hayajneh, Hikmat Ullah, Shahid Mumtaz
Summary: The demand for innovative solutions in biomedical systems for precise diagnosis and management of critical diseases is increasing. A promising technology, non-invasive and intelligent Internet of Medical Things (IoMT) system, emerges to assess patients with reduced health risks. This research introduces a comprehensive framework for early diagnosis of respiratory abnormalities through RF sensing and SDR technology. The results highlight the superior performance of deep learning frameworks in classifying respiratory anomalies.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Shichen Huang, Weina Fu, Zhaoyue Zhang, Shuai Liu
Summary: In the era of adversarial machine learning (AML), developing robust and generalized algorithms has become a key research topic. This study proposes a global similarity matching module and a global-local cognition fusion training mechanism based on relationship adversarial sample generation to improve image-text matching algorithm. Experimental results show significant improvements in accuracy and robustness, performing well in facing security challenges and promoting the fusion of visual and linguistic modalities.
INFORMATION FUSION
(2024)