Multimodal Medical Image Fusion Utilizing Two-scale Image Decomposition via Saliency Detection

被引:1
|
作者
Kaur, Harmanpreet [1 ]
Vig, Renu [1 ]
Kumar, Naresh [1 ]
Sharma, Apoorav [1 ]
Dogra, Ayush [2 ]
Goyal, Bhawna [3 ]
机构
[1] Panjab Univ, UIET, Dept Elect & Commun Engn, Chandigarh 160014, India
[2] Chitkara Univ, Inst Engn & Technol, Rajpura, Punjab, India
[3] Chandigarh Univ, Dept UCRD & ECE, Mohali 140413, Punjab, India
关键词
Multi-modal image fusion; Multi-scale decomposition; Research precision; Medical practice; CT\MRI; Saliency detection; SYSTEM; MRI;
D O I
10.2174/0115734056260083230924154700
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Background: Modern medical imaging modalities used by clinicians have many applications in the diagnosis of complicated diseases. These imaging technologies reveal the internal anatomy and physiology of the body. The fundamental idea behind medical image fusion is to increase the image's global and local contrast, enhance the visual impact, and change its format so that it is better suited for computer processing or human viewing while preventing noise magnification and accomplishing excellent real-time performance. Objective: The top goal is to combine data from various modal images (CT/MRI and MR-T1/MR-T2) into a solitary image that, to the greatest degree possible, retains the key characteristics (prominent features) of the source images. Methods: The clinical accuracy of medical issues is compromised because innumerable classical fusion methods struggle to conserve all the prominent features of the original images. Furthermore, complex implementation, high computation time, and more memory requirements are key problems of transform domain methods. With the purpose of solving these problems, this research suggests a fusion framework for multimodal medical images that makes use of a multi-scale edge-preserving filter and visual saliency detection. The source images are decomposed using a two-scale edge-preserving filter into base and detail layers. Base layers are combined using the addition fusion rule, while detail layers are fused using weight maps constructed using the maximum symmetric surround saliency detection algorithm. Results: The resultant image constructed by the presumed method has improved objective evaluation metrics than other classical methods, as well as unhindered edge contour, more global contrast, and no ringing effect or artifacts. Conclusion: The methodology offers a dominant and symbiotic arsenal of clinical symptomatic, therapeutic, and biomedical research competencies that have the prospective to considerably strengthen medical practice and biological understanding.
引用
收藏
页数:13
相关论文
共 50 条
  • [31] Advancing multimodal medical image fusion: an adaptive image decomposition approach based on multilevel Guided filtering
    Moghtaderi, Shiva
    Einlou, Mokarrameh
    Wahid, Khan A.
    Lukong, Kiven Erique
    ROYAL SOCIETY OPEN SCIENCE, 2024, 11 (04):
  • [32] Benchmarking Saliency Detection Methods on Multimodal Image Data
    Anzid, Hanan
    Le Goic, Gaetan
    Bekkari, Aissam
    Mansouri, Alamin
    Mammass, Driss
    IMAGE AND SIGNAL PROCESSING (ICISP 2018), 2018, 10884 : 11 - 18
  • [33] A bio-inspired two-scale complementarity evaluation method for image fusion
    Hu, Yanxiang
    Zhang, Bo
    INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING, 2017, 15 (06)
  • [34] Multimodal Medical Image Fusion Based on Fuzzy Discrimination With Structural Patch Decomposition
    Yang, Yong
    Wu, Jiahua
    Huang, Shuying
    Fang, Yuming
    Lin, Pan
    Que, Yue
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2019, 23 (04) : 1647 - 1660
  • [35] MATR: Multimodal Medical Image Fusion via Multiscale Adaptive Transformer
    Tang, Wei
    He, Fazhi
    Liu, Yu
    Duan, Yansong
    IEEE Transactions on Image Processing, 2022, 31 : 5134 - 5149
  • [36] MATR: Multimodal Medical Image Fusion via Multiscale Adaptive Transformer
    Tang, Wei
    He, Fazhi
    Liu, Yu
    Duan, Yansong
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2022, 31 : 5134 - 5149
  • [37] A New Polarized Image Fusion Algorithm Based on Two-scale Guided Filtering
    Xie, Fei
    Chen, Jiajia
    2020 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC), 2020, : 1150 - 1155
  • [38] A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion
    Paul, Sudip
    Jain, Shruti
    RECENT ADVANCES IN INFLAMMATION & ALLERGY DRUG DISCOVERY, 2024, 18 (02): : 140 - 155
  • [39] Infrared and visible image fusion via saliency analysis and local edge-preserving multi-scale decomposition
    Zhang, Xiaoye
    Ma, Yong
    Fan, Fan
    Zhang, Ying
    Huang, Jun
    JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 2017, 34 (08) : 1400 - 1410
  • [40] Multimodal Image Fusion Algorithm Based on Two-stage Multiscale Decomposition
    Zhang, Lei
    Cheng, Yi-Yuan
    Li, Jing
    Wang, Zhi-She
    Ji, Lin-Na
    Liu, Wei
    Journal of Computers (Taiwan), 2024, 35 (05) : 15 - 33