Medical Imaging Fusion: A Teaching-Learning Simulation Environment
Commenced in January 2007
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Medical Imaging Fusion: A Teaching-Learning Simulation Environment

Authors: Cristina M. R. Caridade, Ana Rita F. Morais

Abstract:

The use of computational tools has become essential in the context of interactive learning, especially in engineering education. In the medical industry, teaching medical image processing techniques is a crucial part of training biomedical engineers, as it has integrated applications with health care facilities and hospitals. The aim of this article is to present a teaching-learning simulation tool, developed in MATLAB using Graphical User Interface, for medical image fusion that explores different image fusion methodologies and processes in combination with image pre-processing techniques. The application uses different algorithms and medical fusion techniques in real time, allowing to view original images and fusion images, compare processed and original images, adjust parameters and save images. The tool proposed in an innovative teaching and learning environment, consists of a dynamic and motivating teaching simulation for biomedical engineering students to acquire knowledge about medical image fusion techniques, necessary skills for the training of biomedical engineers. In conclusion, the developed simulation tool provides a real-time visualization of the original and fusion images and the possibility to test, evaluate and progress the student’s knowledge about the fusion of medical images. It also facilitates the exploration of medical imaging applications, specifically image fusion, which is critical in the medical industry. Teachers and students can make adjustments and/or create new functions, making the simulation environment adaptable to new techniques and methodologies.

Keywords: Image fusion, image processing, teaching-learning simulation tool, biomedical engineering education.

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References:


[1] Y´a˜nez-M´arquez, Cornelio, et al. Emerging computational tools: Impact on engineering education and computer science learning. International Journal of Engineering Education 30.3 (2014): 533-542.
[2] Ystanbullu, A. Guller, Y. Computer assisted learning for biomedical engineering education: Tools. Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE. 2001. P. 4030-4031.
[3] Amon, T. Valencic, V. VRML Enhanced learning in biology and medicine. Future Generation Computer Systems, v. 17, n. 1, p. 1-6, 2000. DOI:10.1016/S0167-739X(99)00097-7.
[4] Guerrero, JF. et al. BioLab: An educational tool for signal processing training in biomedical engineering education, IEEE Transactions, v. 50, n. 1, p. 34-40, 2007.
[5] Zhou, Y.; Yu, L.; Zhi, C.; Huang, C.; Wang, S.; Zhu, M.; Ke, Z.; Gao, Z.; Zhang, Y.; Fu, S.: A Survey of Multi-Focus Image Fusion Methods. Appl. Sci. 2022, 12, 6281. https://doi.org/10.3390/app12126281
[6] Montesinos, L., Santos-Diaz, A., Salinas-Navarro, D. E., Cendejas-Zaragoza, L. (2022). Experiential learning in biomedical engineering education using wearable devices: a case study in a biomedical signals and systems analysis course. Education Sciences, 12(9), 598.
[7] Lozano-Dur´an, A., Rudolphi-Solero, T., Nava-Baro, E., Ruiz-G´omez, M. J., Sendra-Portero, F. (2023). Training Scientific Communication Skills on Medical Imaging within the Virtual World Second Life: Perception of Biomedical Engineering Students. International Journal of Environmental Research and Public Health, 20(3), 1697.
[8] Buffinton, C.M., Baish, J.W. Ebenstein, D.M. An Introductory Module in Medical Image Segmentation for BME Students. Biomed Eng Education 3, 95–109 (2023). https://doi.org/10.1007/s43683-022-00085-0.
[9] A.P. James, B. V. Dasarathy, Medical Image Fusion: A survey of the state of the art, Information Fusion, 2014
[10] The MathWorks Inc. (2023). MATLAB version: 9.13.0 (R2023a), Natick, Massachusetts: The MathWorks Inc. https://www.mathworks.com
[11] Li, J., Luo, J., Ming, D., Shen, Z., “A New Method for Merging IKONOS Panchromatic and Multispectral Image Data”, IEEE Publications, 2005, pp. 3916 – 3919.
[12] Brock, K. K., Mutic, S., McNutt, T. R., Li, H., Kessler, M. L.: Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132. Medical physics, 44(7), e43-e76. (2017)
[13] An´alise de Imagens M´edicas por Tomografia. Visite in june 2023. http: //www.ic.uff.br/∼aconci/Tomografia.html
[14] James, A.P., Dasarathy, B.V., ”Medical Image Fusion: A survey of the state of the art”, Information Fusion, 2014. https://arxiv.org/ftp/arxiv/ papers/1401/1401.0166.pdf