Abstract
In the last two decades, significant advancement occurs related to medical imaging modalities and image processing techniques. In biomedical imaging, the accuracy of a diagnosed area of interest can be increased using a multimodal dataset of patients. A lot of research and techniques are proposed for processing and analysis of multimodal imaging, which requires datasets for benchmarking and validation of their performances. In this connection, two important databases: MIDAS and OASIS are selected and evaluated for the guidance of the researcher to perform their results in the field of multimodal imaging. The associated diseases to these datasets and open issues in the field of multimodal imaging are also discussed. The main objective of this article is to discuss the current interest of the researcher and open platforms for future research in multimodal medical imaging. We originate some statistical results of graphs and charts using the online Web Analysis tool “SIMILIARWEB” to show public interest on these databases and also arranged these datasets according to various modalities, body scanned areas, disease-based and classification of images to motivate researchers working in multimodal medical areas. The significance of these databases in the field of multimodal image processing is encapsulated by graphical charts and statistical results.
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References
Rajalingam, B., Priya, D.R.: Hybrid multimodality medical image fusion technique for feature enhancement in medical diagnosis. Int. J. Eng. Sci. Invention (IJESI) 2, 52–60 (2018)
Decencière, E., et al.: Feedback on a publicly distributed image database: the Messidor database. Image Anal. Stereology 33(3), 231–234 (2014)
Müller, H., Unay, D.: Retrieval from and understanding of large-scale multi-modal medical datasets: a review. IEEE Trans. Multimedia 19(9), 2093–2104 (2017)
Alam, F., Rahman, S.U.: Challenges and solutions in multimodal medical image subregion detection and registration. J. Med. Imaging Radiat. Sci. 50(1), 24–30 (2018)
Guo, Z., Li, X., Huang, H., Guo, N., Li, Q.: Deep learning-based image segmentation on multimodal medical imaging. IEEE Trans. Radiat. Plasma Med. Sci. 3(2), 162–169 (2019)
Rajalingam, B., Priya, R.: Review of multimodality medical image fusion using combined transform techniques for clinical application. Int. J. Sci. Res. Comput. Sci. Appl. Manage. Stud. 7(3) (2018)
Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19(9), 1498–1507 (2007)
Marcus, D.S., Fotenos, A.F., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. J. Cogn. Neurosci. 22(12), 2677–2684 (2010)
Salehi, S.S., Erdogmus, D., Gholipour, A.: Auto-context convolutional neural network (auto-net) for brain extraction in magnetic resonance imaging. IEEE Trans. Med. Imaging 36(11), 2319–2330 (2017)
Pang, S., Orgun, M.A., Yu, Z.: A novel biomedical image indexing and retrieval system via deep preference learning. Comput. Methods Programs Biomed. 158, 53–69 (2018)
Pang, S., Du, A., Orgun, M.A., Yu, Z.: A novel fused convolutional neural network for biomedical image classification. Med. Biol. Eng. Comput. 57(1), 107–121 (2019)
Mahapatra, D., Antony, B., Sedai, S., Garnavi, R.: Deformable medical image registration using generative adversarial networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI), Washington, DC, USA, pp. 1449–1453. IEEE (2018)
Moghbel, M., Mashohor, S., Mahmud, R., Saripan, M.I.: Review of liver segmentation and computer assisted detection/diagnosis methods in computed tomography. Artif. Intell. Rev. 50(4), 497–537 (2018)
Dar, S., Yurt, M., Karacan, L., Erdem, A., Erdem, E., Çukur, T.: Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Trans. Med. Imaging 38(10), 2375–2388 (2019)
Islam, J., Zhang, Y.: A novel deep learning based multi-class classification method for Alzheimer’s disease detection using brain MRI data. In: Zeng, Y., et al. (eds.) BI 2017. LNCS (LNAI), vol. 10654, pp. 213–222. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70772-3_20
Hon, M., Khan, NM.: Towards Alzheimer’s disease classification through transfer learning. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, pp. 1166–1169. IEEE (2017)
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Azam, M.A., Khan, K.B., Aqeel, M., Chishti, A.R., Abbasi, M.N. (2020). Analysis of the MIDAS and OASIS Biomedical Databases for the Application of Multimodal Image Processing. In: Bajwa, I., Sibalija, T., Jawawi, D. (eds) Intelligent Technologies and Applications. INTAP 2019. Communications in Computer and Information Science, vol 1198. Springer, Singapore. https://doi.org/10.1007/978-981-15-5232-8_50
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DOI: https://doi.org/10.1007/978-981-15-5232-8_50
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