Digital image fusion has advanced significantly in governments and civil domains since its introduction in the late 1980s, certainly image fusion of infrared light, materials characterization, remote sensing data fusion, visions segmentation techniques, and brain tumor detection fusion. In medical diagnostics, imaging technology is critical. Because single medical pictures cannot match the demands of diagnostic techniques, which necessitate a huge quantity of data, image fusion study has become a hot subject. Single-mode integration and multi - modal fusion is the two types of medical image processing. Due to the limitations of single-modal fusion's data, many scientists are investigating multidimensional fusion. Brain tumor detection fusion represents the operations of integrating multiple images from imaging modality to formulate fused images with larger volume of data, allowing medical images to be more clinically useful. In this article, we focus on providing a survey of multi-modal image fusion approaches with central focus on novel developments in the domain based on the present fusion approaches, incorporating deep learning fusion approaches. Lastly, this concludes that contemporary multi-modal image fusion study findings are significantly fundamental, and the development trends is on the increase, however there are several hurdles in the study area.
P. Sayanna, "A Novel Digital Watermarking Approach for Accurate Authenticati on Using of Integer Wavelet Transform Coefficients", International Journal Of Engineering And Computer Science, 2016. Doi: 10.18535/ijecs/v5i9.05.
Y. Xu and Y. Lu, "Adaptive weighted fusion: A novel fusion approach for image classification", Neurocomputing, vol. 168, pp. 566-574, 2015. Doi: 10.1016/j.neucom.2015.05.070.
M. Huter, C. Jensch and J. Strube, "Model Validation and Process Design of Continuous Single Pass Tangential Flow Filtration Focusing on Continuous Bioprocessing for High Protein Concentrations", Processes, vol. 7, no. 11, p. 781, 2019. Doi: 10.3390/pr7110781.
R. Redondo, F. Šroubek, S. Fischer and G. Cristóbal, "Multifocus image fusion using the log-Gabor transform and a Multisize Windows technique", Information Fusion, vol. 10, no. 2, pp. 163-171, 2009. Doi: 10.1016/j.inffus.2008.08.006.
R. Khire, Y. Bahei-El-Din and P. Hajela, "Multiscale Transformation Field Analysis of Progressive Damage in Fibrous Laminates", International Journal for Multiscale Computational Engineering, vol. 8, no. 1, pp. 69-80, 2010. Doi: 10.1615/intjmultcompeng.v8.i1.60.
S. Mase, "Marked Gibbs Processes and Asymptotic Normality of Maximum Pseudo-Likelihood Estimators", Mathematische Nachrichten, vol. 209, no. 1, pp. 151-169, 2000. Doi: 10.1002/(sici)1522-2616(200001)209:1<151::aid-mana151>3.0.co;2-j.
N. BinHannan, M. Abdul Mottalib, S. Jeeshan Kabeer and A. Muhammad Sultan, "MFS-PSO: A Modified PSO Method for Optimizing Gene Selection", International Journal of Computer Applications, vol. 67, no. 1, pp. 38-42, 2013. Doi: 10.5120/11363-6595.
N. Wang, Y. Ma, W. Wang and S. Zhou, "An Image Fusion Method Based on NSCT and Dual-channel PCNN Model", Journal of Networks, vol. 9, no. 2, 2014. Doi: 10.4304/jnw.9.2.501-506.
Z. Wang and Y. Ma, "Medical image fusion using m-PCNN", Information Fusion, vol. 9, no. 2, pp. 176-185, 2008. Doi: 10.1016/j.inffus.2007.04.003.
R. Srivastava and D. Mishra, "Comparison between FPGA Implementation of Discrete Wavelet Transform, Dual Tree Complex Wavelet Transform and Double Density Dual Tree Complex Wavelet Transform in Verilog HDL", International Journal of Trend in Scientific Research and Development, vol. -2, no. -4, pp. 1153-1156, 2018. Doi: 10.31142/ijtsrd14108.
L. Tian, D. Zheng and C. Zhu, "Image Classification Based on the Combination of Text Features and Visual Features", International Journal of Intelligent Systems, vol. 28, no. 3, pp. 242-256, 2012. Doi: 10.1002/int.21567.
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Ram Saraswat
Ram Saraswat
Jawaharlal Nehru University, JNU Ring Rd, New Delhi, Delhi 110067.
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Cite this article
Ram Saraswat, “A Survey of Multi-Modal Image Fusion Methodologies”, Journal of Biomedical and Sustainable Healthcare Applications, vol.1, no.2, pp. 132-140, July 2021. doi: 10.53759/0088/JBSHA202101015.
