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Li K, Yao J, Zhao P, Luo Y, Ge X, Yang R, Cheng X, Miao X. Ovonic threshold switching-based artificial afferent neurons for thermal in-sensor computing. MATERIALS HORIZONS 2024; 11:2106-2114. [PMID: 38545857 DOI: 10.1039/d4mh00053f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Artificial afferent neurons in the sensory nervous system inspired by biology have enormous potential for efficiently perceiving and processing environmental information. However, the previously reported artificial afferent neurons suffer from two prominent challenges: considerable power consumption and limited scalability efficiency. Herein, addressing these challenges, a bioinspired artificial thermal afferent neuron based on a N-doped SiTe ovonic threshold switching (OTS) device is presented for the first time. The engineered OTS device shows remarkable uniformity and robust endurance, ensuring the reliability and efficacy of the artificial afferent neurons. A substantially decreased leakage current of the SiTe OTS device by nitrogen doping results in ultra-low power consumption less than 0.3 nJ per spike for artificial afferent neurons. The inherent temperature response exhibited by N-doped SiTe OTS materials allows us to construct a highly compact artificial thermal afferent neuron over a wide temperature range. An edge detection task is performed to further verify its thermal perceptual computing function. Our work provides an insight into OTS-based artificial afferent neurons for electronic skin and sensory neurorobotics.
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Affiliation(s)
- Kai Li
- School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Jiaping Yao
- School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Peng Zhao
- School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Yunhao Luo
- School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Xiang Ge
- School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Rui Yang
- School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
- Hubei Yangtze Memory Laboratories, Wuhan 430205, China
| | - Xiaomin Cheng
- School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
- Hubei Yangtze Memory Laboratories, Wuhan 430205, China
| | - Xiangshui Miao
- School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
- Hubei Yangtze Memory Laboratories, Wuhan 430205, China
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Hu Q, Cai W, Xu S, Hu S, Wang L, He X. Adaptive convolutional sparsity with sub-band correlation in the NSCT domain for MRI image fusion. Phys Med Biol 2024; 69:055022. [PMID: 38316044 DOI: 10.1088/1361-6560/ad2636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 02/05/2024] [Indexed: 02/07/2024]
Abstract
Objective.Multimodal medical image fusion (MMIF) technologies merges diverse medical images with rich information, boosting diagnostic efficiency and accuracy. Due to global optimization and single-valued nature, convolutional sparse representation (CSR) outshines the standard sparse representation (SR) in significance. By addressing the challenges of sensitivity to highly redundant dictionaries and robustness to misregistration, an adaptive convolutional sparsity scheme with measurement of thesub-band correlationin the non-subsampled contourlet transform (NSCT) domain is proposed for MMIF.Approach.The fusion scheme incorporates four main components: image decomposition into two scales, fusion of detail layers, fusion of base layers, and reconstruction of the two scales. We solved a Tikhonov regularization optimization problem with source images to obtain the base and detail layers. Then, after CSR processing, detail layers were sparsely decomposed using pre-trained dictionary filters for initial coefficient maps. NSCT domain'ssub-band correlationwas used to refine fusion coefficient maps, and sparse reconstruction produced the fused detail layer. Meanwhile, base layers were fused using averaging. The final fused image was obtained via two-scale reconstruction.Main results.Experimental validation of clinical image sets revealed that the proposed fusion scheme can not only effectively eliminate the interference of partial misregistration, but also outperform the representative state-of-the-art fusion schemes in the preservation of structural and textural details according to subjective visual evaluations and objective quality evaluations.Significance. The proposed fusion scheme is competitive due to its low-redundancy dictionary, robustness to misregistration, and better fusion performance. This is achieved by training the dictionary with minimal samples through CSR to adaptively preserve overcompleteness for detail layers, and constructing fusion activity level withsub-band correlationin the NSCT domain to maintain CSR attributes. Additionally, ordering the NSCT for reverse sparse representation further enhancessub-band correlationto promote the preservation of structural and textural details.
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Affiliation(s)
- Qiu Hu
- School of Information Science and Engineering, NingboTech University, Ningbo 315100, People's Republic of China
| | - Weiming Cai
- School of Information Science and Engineering, NingboTech University, Ningbo 315100, People's Republic of China
- Zhejiang Engineering Research Center for Intelligent Marine Ranch Equipment, Ningbo 315100, People's Republic of China
| | - Shuwen Xu
- Third Research Institute of China Electronics Technology Group Corporation, Beijing 100846, People's Republic of China
| | - Shaohai Hu
- Institute of Information Science, Beijing Jiaotong University, Beijing 100044, People's Republic of China
- Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, People's Republic of China
| | - Lang Wang
- School of Information Science and Engineering, NingboTech University, Ningbo 315100, People's Republic of China
| | - Xinyi He
- Ningbo Xiaoshi High School, Ningbo 315100, People's Republic of China
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Behrouzi Y, Basiri A, Pourgholi R, Kiaei AA. Fusion of medical images using Nabla operator; Objective evaluations and step-by-step statistical comparisons. PLoS One 2023; 18:e0284873. [PMID: 37585476 PMCID: PMC10431637 DOI: 10.1371/journal.pone.0284873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 04/11/2023] [Indexed: 08/18/2023] Open
Abstract
Since vectors include direction and magnitude, they have more information than scalars. So, converting the scalar images into the vector field leads achieving much information about the images that have been hidden in the spatial domain. In this paper, the proposed method fuses images after transforming the scalar field of images to a vector one. To transform the field, it uses Nabla operator. After that, the inverse transform is implemented to reconstruct the fused medical image. To show the performance of the proposed method and to evaluate it, different experiments and statistical comparisons were accomplished. Comparing the experimental results with the previous works, shows the effectiveness of the proposed method.
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Affiliation(s)
- Yasin Behrouzi
- School of Mathematics and Computer Science, Damghan University, Damghan, Iran
| | - Abdolali Basiri
- School of Mathematics and Computer Science, Damghan University, Damghan, Iran
| | - Reza Pourgholi
- School of Mathematics and Computer Science, Damghan University, Damghan, Iran
| | - Ali Akbar Kiaei
- Department of Computer Engineering, Bu-ali Sina University, Hamedan, Iran
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Diwakar M, Singh P, Singh R, Sisodia D, Singh V, Maurya A, Kadry S, Sevcik L. Multimodality Medical Image Fusion Using Clustered Dictionary Learning in Non-Subsampled Shearlet Transform. Diagnostics (Basel) 2023; 13:diagnostics13081395. [PMID: 37189496 DOI: 10.3390/diagnostics13081395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 03/23/2023] [Accepted: 03/24/2023] [Indexed: 05/17/2023] Open
Abstract
Imaging data fusion is becoming a bottleneck in clinical applications and translational research in medical imaging. This study aims to incorporate a novel multimodality medical image fusion technique into the shearlet domain. The proposed method uses the non-subsampled shearlet transform (NSST) to extract both low- and high-frequency image components. A novel approach is proposed for fusing low-frequency components using a modified sum-modified Laplacian (MSML)-based clustered dictionary learning technique. In the NSST domain, directed contrast can be used to fuse high-frequency coefficients. Using the inverse NSST method, a multimodal medical image is obtained. Compared to state-of-the-art fusion techniques, the proposed method provides superior edge preservation. According to performance metrics, the proposed method is shown to be approximately 10% better than existing methods in terms of standard deviation, mutual information, etc. Additionally, the proposed method produces excellent visual results regarding edge preservation, texture preservation, and more information.
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Affiliation(s)
- Manoj Diwakar
- Department of Computer Science and Engineering, Graphic Era (Deemed to Be University), Dehradun 248002, Uttarakhand, India
| | - Prabhishek Singh
- School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, Uttar Pradesh, India
| | - Ravinder Singh
- Department of Computer Science and Engineering, Engineering College Ajmer, Ajmer 305025, Rajasthan, India
| | - Dilip Sisodia
- Department of Computer Science and Engineering, Engineering College Ajmer, Ajmer 305025, Rajasthan, India
| | - Vijendra Singh
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India
| | - Ankur Maurya
- School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, Uttar Pradesh, India
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon
| | - Lukas Sevcik
- University of Zilina, Univerzitna 1, 01026 Zilina, Slovakia
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Xu W, Fu YL, Xu H, Wong KKL. Medical image fusion using enhanced cross-visual cortex model based on artificial selection and impulse-coupled neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107304. [PMID: 36586176 DOI: 10.1016/j.cmpb.2022.107304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 11/28/2022] [Accepted: 12/08/2022] [Indexed: 06/17/2023]
Abstract
OBJECTIVE The traditional ICM is widely used in applications, such as image edge detection and image segmentation. However, several model parameters must be set, which tend to lead to reduced accuracy and increased cost. As medical images have more complex edges, contours and details, more suitable combinatorial algorithms are needed to handle the pathological diagnosis of multiple cerebral infarcts and acute strokes, resulting in the findings being more applicable, as well as having good clinical value. METHODS To better solve the medical image fusion and diagnosis problems, this paper introduces the image fusion algorithm based on the combination of NSCT and improved ICM and proposes low-frequency, sub-band fusion rules and high-frequency sub-band fusion rules. The above method is applied to the fusion of CT/MRI images, subsequently, three other fusion algorithms, including NSCT-SF-PCNN, NSCT-SR-PCNN and Adaptive-PCNN are compared, and the simulation results of image fusion are analyzed and validated. RESULTS According to the experimental findings, the suggested algorithm performs better than other fusion algorithms in terms of five objective evaluation metrics or subjective evaluation. The NSCT transform and the improved ICM were combined, and the outcomes were evaluated against those of other fusion algorithms. The CT/MRI medical images of healthy brain tissue, numerous cerebral infarcts and acute strokes were combined using this technique. CONCLUSION Medical image fusion using Adaptive-PCNN produces satisfactory results, not only in relation to improved image clarity but also in terms of outstanding edge information, high contrast and brightness.
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Affiliation(s)
- Wanni Xu
- Xiamen Academy of Arts and Design, Fuzhou University, Xiamen 361024, China; Department of Computer Information Engineering, Nanchang Institute of Technology, Nanchang 330044, China
| | - You-Lei Fu
- Department of Computer Information Engineering, Nanchang Institute of Technology, Nanchang 330044, China; Fine Art and Design College, Quanzhou Normal University, Quanzhou 362000, China.
| | - Huasen Xu
- Department of Civil Engineering, Shanghai Normal University, Shanghai 201418, China.
| | - Kelvin K L Wong
- Fine Art and Design College, Quanzhou Normal University, Quanzhou 362000, China
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Tawfik N, Elnemr HA, Fakhr M, Dessouky MI, El-Samie FEA. Multimodal Medical Image Fusion Using Stacked Auto-encoder in NSCT Domain. J Digit Imaging 2022; 35:1308-1325. [PMID: 35768753 PMCID: PMC9582113 DOI: 10.1007/s10278-021-00554-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 11/16/2021] [Accepted: 11/18/2021] [Indexed: 11/24/2022] Open
Abstract
Medical image fusion is a process that aims to merge the important information from images with different modalities of the same organ of the human body to create a more informative fused image. In recent years, deep learning (DL) methods have achieved significant breakthroughs in the field of image fusion because of their great efficiency. The DL methods in image fusion have become an active topic due to their high feature extraction and data representation ability. In this work, stacked sparse auto-encoder (SSAE), a general category of deep neural networks, is exploited in medical image fusion. The SSAE is an efficient technique for unsupervised feature extraction. It has high capability of complex data representation. The proposed fusion method is carried as follows. Firstly, the source images are decomposed into low- and high-frequency coefficient sub-bands with the non-subsampled contourlet transform (NSCT). The NSCT is a flexible multi-scale decomposition technique, and it is superior to traditional decomposition techniques in several aspects. After that, the SSAE is implemented for feature extraction to obtain a sparse and deep representation from high-frequency coefficients. Then, the spatial frequencies are computed for the obtained features to be used for high-frequency coefficient fusion. After that, a maximum-based fusion rule is applied to fuse the low-frequency sub-band coefficients. The final integrated image is acquired by applying the inverse NSCT. The proposed method has been applied and assessed on various groups of medical image modalities. Experimental results prove that the proposed method could effectively merge the multimodal medical images, while preserving the detail information, perfectly.
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Affiliation(s)
- Nahed Tawfik
- Computers and Systems Department, Electronics Research Institute, Joseph Tito St, El Nozha, Huckstep Cairo, Egypt.
| | - Heba A Elnemr
- Department of Computer and Software Engineering, Misr University for Science and Technology, Giza, Egypt
| | - Mahmoud Fakhr
- Computers and Systems Department, Electronics Research Institute, Joseph Tito St, El Nozha, Huckstep Cairo, Egypt
| | - Moawad I Dessouky
- Electronics and Electrical Communications Department, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - Fathi E Abd El-Samie
- Electronics and Electrical Communications Department, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
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7
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A new approach of multi-modal medical image fusion using intuitionistic fuzzy set. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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8
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Multimodal medical image fusion with convolution sparse representation and mutual information correlation in NSST domain. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00792-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
AbstractMultimodal medical image is an effective method to solve a series of clinical problems, such as clinical diagnosis and postoperative treatment. In this study, a medical image fusion method based on convolutional sparse representation (CSR) and mutual information correlation is proposed. In this method, the source image is decomposed into one high-frequency and one low-frequency sub-band by non-subsampled shearlet transform. For the high-frequency sub-band, CSR is used for high-frequency coefficient fusion. For the low-frequency sub-band, different fusion strategies are used for different regions by mutual information correlation analysis. Analysis of two kinds of medical image fusion problems, namely, CT–MRI and MRI–SPECT, reveals that the performance of this method is robust in terms of five common objective metrics. Compared with the other six advanced medical image fusion methods, the experimental results show that the proposed method achieves better results in subjective vision and objective evaluation metrics.
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Ramlal SD, Sachdeva J, Ahuja CK, Khandelwal N. Multimodal Medical Image Fusion Using Nonsubsampled Shearlet Transform and Smallest Uni-Value Segment Assimilating Nucleus. INT J PATTERN RECOGN 2022. [DOI: 10.1142/s0218001422570014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper presents a new fusion scheme for medical (CT-MRI) images which is based on the nonsubsampled shearlet transform (NSST). The various image pairs to be fused are obtained from primary and internet sources. Initially, the images are decomposed through NSST into general and detailed features. The smallest uni-value segment assimilating nucleus (SUSAN) and local sum of Gaussian weighted pixel intensities-based activity measures are proposed to fuse the detailed sub-bands and low-frequency sub-band of NSST, respectively, for faster execution of the algorithm. Visual and parametric comparison of the proposed scheme is done through five traditional fusion algorithms using nine fusion performance parameters. In addition, Wilcoxon signed ranks test is also applied to compare different methods scientifically with the proposed fusion scheme. It is observed that the presented method is better in retaining bone, calcification, cerebrospinal fluid (CSF), edema and tumor details of the source images and is faster than other classical fusion schemes. The fused images of the proposed method are suitable for locating the site of biopsy externally or incision location in the bone of the brain skull with minimum diagnostic time.
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Affiliation(s)
- Sharma Dileepkumar Ramlal
- Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India
- Electronics and Telecommunication Engineering Department, Eternal University, Baru Sahib, H.P., India
- Chitkara University Institute of Engineering & Technology, Baddi, HP, India
| | - Jainy Sachdeva
- Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India
| | - Chirag Kamal Ahuja
- Department of Radio-Diagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India
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Yin XX, Hadjiloucas S, Sun L, Bowen JW, Zhang Y. A Review on the Rule-Based Filtering Structure with Applications on Computational Biomedical Images. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:2599256. [PMID: 35299677 PMCID: PMC8923774 DOI: 10.1155/2022/2599256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 01/27/2022] [Indexed: 11/17/2022]
Abstract
In this paper, we present rule-based fuzzy inference systems that consist of a series of mathematical representations based on fuzzy concepts in the filtering structure. It is crucial for understanding and discussing different principles associated with fuzzy filter design procedures. A number of typical fuzzy multichannel filtering approaches are provided in order to clarify the different fuzzy filter designs and compare different algorithms. In particular, in most practical applications (i.e., biomedical image analysis), the emphasis is placed primarily on fuzzy filtering algorithms, with the main advantages of restoration of corrupted medical images and the interpretation capability, along with the capability of edge preservation and relevant image information for accurate diagnosis of diseases.
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Affiliation(s)
- Xiao-Xia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
| | - Sillas Hadjiloucas
- Biomedical Engineering, School of Biological Sciences, University of Reading, Reading RG6 6AY, UK
| | - Le Sun
- Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China
| | - John W. Bowen
- Biomedical Engineering, School of Biological Sciences, University of Reading, Reading RG6 6AY, UK
| | - Yanchun Zhang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
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Wang T, Chen Y, Du H, Liu Y, Zhang L, Meng M. Monitoring of Neuroendocrine Changes in Acute Stage of Severe Craniocerebral Injury by Transcranial Doppler Ultrasound Image Features Based on Artificial Intelligence Algorithm. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:3584034. [PMID: 34956395 PMCID: PMC8694971 DOI: 10.1155/2021/3584034] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/01/2021] [Accepted: 11/10/2021] [Indexed: 11/18/2022]
Abstract
This study was aimed at exploring the application value of transcranial Doppler (TCD) based on artificial intelligence algorithm in monitoring the neuroendocrine changes in patients with severe head injury in the acute phase; 80 patients with severe brain injury were included in this study as the study subjects, and they were randomly divided into the control group (conventional TCD) and the experimental group (algorithm-optimized TCD), 40 patients in each group. An artificial intelligence neighborhood segmentation algorithm for TCD images was designed to comprehensively evaluate the application value of this algorithm by measuring the TCD image area segmentation error and running time of this algorithm. In addition, the Glasgow coma scale (GCS) and each neuroendocrine hormone level were used to assess the neuroendocrine status of the patients. The results showed that the running time of the artificial intelligence neighborhood segmentation algorithm for TCD was 3.14 ± 1.02 s, which was significantly shorter than 32.23 ± 9.56 s of traditional convolutional neural network (CNN) algorithms (P < 0.05). The false rejection rate (FRR) of TCD image area segmentation of this algorithm was significantly reduced, and the false acceptance rate (FAR) and true acceptance rate (TAR) were significantly increased (P < 0.05). The consistent rate of the GCS score and Doppler ultrasound imaging diagnosis results in the experimental group was 93.8%, which was significantly higher than the 80.3% in the control group (P < 0.05). The consistency rate of Doppler ultrasound imaging diagnosis results of patients in the experimental group with abnormal levels of follicle stimulating hormone (FSH), prolactin (PRL), growth hormone (GH), adrenocorticotropic hormone (ACTH), and thyroid stimulating hormone (TSH) was significantly higher than that of the control group (P < 0.05). In summary, the artificial intelligence neighborhood segmentation algorithm can significantly shorten the processing time of the TCD image and reduce the segmentation error of the image area, which significantly improves the monitoring level of TCD for patients with severe craniocerebral injury and has good clinical application value.
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Affiliation(s)
- Tao Wang
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 201801, China
| | - Yizhu Chen
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 201801, China
| | - Hangxiang Du
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 201801, China
| | - Yongan Liu
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 201801, China
| | - Lidi Zhang
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 201801, China
| | - Mei Meng
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 201801, China
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12
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CT and MRI image fusion algorithm based on hybrid ℓ0ℓ1 layer decomposing and two-dimensional variation transform. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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13
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Chen J, Chen L, Shabaz M. Image Fusion Algorithm at Pixel Level Based on Edge Detection. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:5760660. [PMID: 34422244 PMCID: PMC8371621 DOI: 10.1155/2021/5760660] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 07/29/2021] [Indexed: 02/07/2023]
Abstract
In the present scenario, image fusion is utilized at a large level for various applications. But, the techniques and algorithms are cumbersome and time-consuming. So, aiming at the problems of low efficiency, long running time, missing image detail information, and poor image fusion, the image fusion algorithm at pixel level based on edge detection is proposed. The improved ROEWA (Ratio of Exponentially Weighted Averages) operator is used to detect the edge of the image. The variable precision fitting algorithm and edge curvature change are used to extract the feature line of the image edge and edge angle point of the feature to improve the stability of image fusion. According to the information and characteristics of the high-frequency region and low-frequency region, different image fusion rules are set. To cope with the high-frequency area, the local energy weighted fusion approach based on edge information is utilized. The low-frequency region is processed by merging the region energy with the weighting factor, and the fusion results of the high findings demonstrate that the image fusion technique presented in this work increases the resolution by 1.23 and 1.01, respectively, when compared to the two standard approaches. When compared to the two standard approaches, the experimental results show that the proposed algorithm can effectively reduce the lack of image information. The sharpness and information entropy of the fused image are higher than the experimental comparison method, and the running time is shorter and has better robustness.
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Affiliation(s)
- Jiming Chen
- School of Computer and Information Science, Hunan Institute of Technology, Hengyang 421002, China
| | - Liping Chen
- School of Computer and Information Science, Hunan Institute of Technology, Hengyang 421002, China
| | - Mohammad Shabaz
- Arba Minch University, Arba Minch, Ethiopia
- Department of Computer Science Engineering, Chitkara University, Chandigarh, India
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14
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Diwakar M, Singh P, Shankar A. Multi-modal medical image fusion framework using co-occurrence filter and local extrema in NSST domain. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102788] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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15
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Das M, Gupta D, Radeva P, Bakde AM. Optimized CT-MR neurological image fusion framework using biologically inspired spiking neural model in hybrid ℓ1 − ℓ0 layer decomposition domain. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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16
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Elzeki OM, Abd Elfattah M, Salem H, Hassanien AE, Shams M. A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset. PeerJ Comput Sci 2021; 7:e364. [PMID: 33817014 PMCID: PMC7959632 DOI: 10.7717/peerj-cs.364] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 12/30/2020] [Indexed: 05/31/2023]
Abstract
BACKGROUND AND PURPOSE COVID-19 is a new strain of viruses that causes life stoppage worldwide. At this time, the new coronavirus COVID-19 is spreading rapidly across the world and poses a threat to people's health. Experimental medical tests and analysis have shown that the infection of lungs occurs in almost all COVID-19 patients. Although Computed Tomography of the chest is a useful imaging method for diagnosing diseases related to the lung, chest X-ray (CXR) is more widely available, mainly due to its lower price and results. Deep learning (DL), one of the significant popular artificial intelligence techniques, is an effective way to help doctors analyze how a large number of CXR images is crucial to performance. MATERIALS AND METHODS In this article, we propose a novel perceptual two-layer image fusion using DL to obtain more informative CXR images for a COVID-19 dataset. To assess the proposed algorithm performance, the dataset used for this work includes 87 CXR images acquired from 25 cases, all of which were confirmed with COVID-19. The dataset preprocessing is needed to facilitate the role of convolutional neural networks (CNN). Thus, hybrid decomposition and fusion of Nonsubsampled Contourlet Transform (NSCT) and CNN_VGG19 as feature extractor was used. RESULTS Our experimental results show that imbalanced COVID-19 datasets can be reliably generated by the algorithm established here. Compared to the COVID-19 dataset used, the fuzed images have more features and characteristics. In evaluation performance measures, six metrics are applied, such as QAB/F, QMI, PSNR, SSIM, SF, and STD, to determine the evaluation of various medical image fusion (MIF). In the QMI, PSNR, SSIM, the proposed algorithm NSCT + CNN_VGG19 achieves the greatest and the features characteristics found in the fuzed image is the largest. We can deduce that the proposed fusion algorithm is efficient enough to generate CXR COVID-19 images that are more useful for the examiner to explore patient status. CONCLUSIONS A novel image fusion algorithm using DL for an imbalanced COVID-19 dataset is the crucial contribution of this work. Extensive results of the experiment display that the proposed algorithm NSCT + CNN_VGG19 outperforms competitive image fusion algorithms.
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Affiliation(s)
- Omar M. Elzeki
- Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt
| | | | - Hanaa Salem
- Communications and Computers Engineering Department, Faculty of Engineering, Delta University for Science and Technology, Gamasa, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
| | - Mahmoud Shams
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt
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17
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Tawfik N, Elnemr HA, Fakhr M, Dessouky MI, Abd El-Samie FE. Survey study of multimodality medical image fusion methods. MULTIMEDIA TOOLS AND APPLICATIONS 2021; 80:6369-6396. [DOI: 10.1007/s11042-020-08834-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 01/13/2020] [Accepted: 03/06/2020] [Indexed: 09/02/2023]
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18
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Wu Q, Dang B, Lu C, Xu G, Yang G, Wang J, Chuai X, Lu N, Geng D, Wang H, Li L. Spike Encoding with Optic Sensory Neurons Enable a Pulse Coupled Neural Network for Ultraviolet Image Segmentation. NANO LETTERS 2020; 20:8015-8023. [PMID: 33063511 DOI: 10.1021/acs.nanolett.0c02892] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Drawing inspiration from biology, neuromorphic systems are of great interest in direct interaction and efficient processing of analogue signals in the real world and could be promising for the development of smart sensors. Here, we demonstrate an artificial sensory neuron consisting of an InGaZnO4 (IGZO4)-based optical sensor and NbOx-based oscillation neuron in series, which can simultaneously sense the optical information even beyond the visible light region and encode them into electrical impulses. Such artificial vision sensory neurons can convey visual information in a parallel manner analogous to biological vision systems, and the output spikes can be effectively processed by a pulse coupled neural network, demonstrating the capability of image segmentation out of a complex background. This study could facilitate the construction of artificial visual systems and pave the way for the development of light-driven neurorobotics, bioinspired optoelectronics, and neuromorphic computing.
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Affiliation(s)
- Quantan Wu
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bingjie Dang
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an 710071, China
- Key Laboratory of Microelectronic Devices and Circuits (MOE), Institute of Microelectronics, Peking University, Beijing 100871, China
| | - Congyan Lu
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guangwei Xu
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guanhua Yang
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiawei Wang
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xichen Chuai
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Nianduan Lu
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Di Geng
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hong Wang
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an 710071, China
| | - Ling Li
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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19
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Wang C, Zhao Z, Ren Q, Xu Y, Yu Y. A novel multi-focus image fusion by combining simplified very deep convolutional networks and patch-based sequential reconstruction strategy. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106253] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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20
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Wang K, Zheng M, Wei H, Qi G, Li Y. Multi-Modality Medical Image Fusion Using Convolutional Neural Network and Contrast Pyramid. SENSORS (BASEL, SWITZERLAND) 2020; 20:E2169. [PMID: 32290472 PMCID: PMC7218740 DOI: 10.3390/s20082169] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 04/06/2020] [Accepted: 04/08/2020] [Indexed: 12/21/2022]
Abstract
Medical image fusion techniques can fuse medical images from different morphologies to make the medical diagnosis more reliable and accurate, which play an increasingly important role in many clinical applications. To obtain a fused image with high visual quality and clear structure details, this paper proposes a convolutional neural network (CNN) based medical image fusion algorithm. The proposed algorithm uses the trained Siamese convolutional network to fuse the pixel activity information of source images to realize the generation of weight map. Meanwhile, a contrast pyramid is implemented to decompose the source image. According to different spatial frequency bands and a weighted fusion operator, source images are integrated. The results of comparative experiments show that the proposed fusion algorithm can effectively preserve the detailed structure information of source images and achieve good human visual effects.
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Affiliation(s)
- Kunpeng Wang
- School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China;
- Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang 621010, China
| | - Mingyao Zheng
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; (M.Z.); (H.W.)
| | - Hongyan Wei
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; (M.Z.); (H.W.)
| | - Guanqiu Qi
- Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA;
| | - Yuanyuan Li
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; (M.Z.); (H.W.)
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21
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Multi-modality medical images fusion based on local-features fuzzy sets and novel sum-modified-Laplacian in non-subsampled shearlet transform domain. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101724] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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22
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Lian J, Yang Z, Sun W, Guo Y, Zheng L, Li J, Shi B, Ma Y. An image segmentation method of a modified SPCNN based on human visual system in medical images. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.12.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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23
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Shahdoosti HR, Tabatabaei Z. MRI and PET/SPECT image fusion at feature level using ant colony based segmentation. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.08.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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24
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Singh S, Anand R. Ripplet domain fusion approach for CT and MR medical image information. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.05.042] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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25
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Liu X, Mei W, Du H. Multi-modality medical image fusion based on image decomposition framework and nonsubsampled shearlet transform. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.10.001] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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26
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Gupta D. Nonsubsampled shearlet domain fusion techniques for CT–MR neurological images using improved biological inspired neural model. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2017.12.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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27
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Zhang X, Ren J, Huang Z, Zhu F. Spiking Cortical Model Based Multimodal Medical Image Fusion by Combining Entropy Information with Weber Local Descriptor. SENSORS (BASEL, SWITZERLAND) 2016; 16:E1503. [PMID: 27649190 PMCID: PMC5038776 DOI: 10.3390/s16091503] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 09/02/2016] [Accepted: 09/09/2016] [Indexed: 11/30/2022]
Abstract
Multimodal medical image fusion (MIF) plays an important role in clinical diagnosis and therapy. Existing MIF methods tend to introduce artifacts, lead to loss of image details or produce low-contrast fused images. To address these problems, a novel spiking cortical model (SCM) based MIF method has been proposed in this paper. The proposed method can generate high-quality fused images using the weighting fusion strategy based on the firing times of the SCM. In the weighting fusion scheme, the weight is determined by combining the entropy information of pulse outputs of the SCM with the Weber local descriptor operating on the firing mapping images produced from the pulse outputs. The extensive experiments on multimodal medical images show that compared with the numerous state-of-the-art MIF methods, the proposed method can preserve image details very well and avoid the introduction of artifacts effectively, and thus it significantly improves the quality of fused images in terms of human vision and objective evaluation criteria such as mutual information, edge preservation index, structural similarity based metric, fusion quality index, fusion similarity metric and standard deviation.
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Affiliation(s)
- Xuming Zhang
- Department of Biomedical Engineering, School of Life Science and Technology, Huazhong University of Science and Technology, No. 1037, Luoyu Road, Wuhan 430074, China.
| | - Jinxia Ren
- Department of Biomedical Engineering, School of Life Science and Technology, Huazhong University of Science and Technology, No. 1037, Luoyu Road, Wuhan 430074, China.
| | - Zhiwen Huang
- Department of Biomedical Engineering, School of Life Science and Technology, Huazhong University of Science and Technology, No. 1037, Luoyu Road, Wuhan 430074, China.
| | - Fei Zhu
- Department of Biomedical Engineering, School of Life Science and Technology, Huazhong University of Science and Technology, No. 1037, Luoyu Road, Wuhan 430074, China.
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