Analysis of the MIDAS and OASIS Biomedical Databases for the Application of Multimodal Image Processing

  • Muhammad Adeel AzamEmail author
  • Khan Bahadar Khan
  • Muhammad Aqeel
  • Abdul Rehman Chishti
  • Muhammad Nawaz Abbasi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1198)


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.


Multimodal imaging Biomedical image databases MIDAS dataset OASIS dataset 


  1. 1.
    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)Google Scholar
  2. 2.
    Decencière, E., et al.: Feedback on a publicly distributed image database: the Messidor database. Image Anal. Stereology 33(3), 231–234 (2014)CrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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) Google Scholar
  7. 7.
  8. 8.
  9. 9.
  10. 10.
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
  14. 14.
    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)CrossRefGoogle Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    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)Google Scholar
  18. 18.
    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)CrossRefGoogle Scholar
  19. 19.
    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)CrossRefGoogle Scholar
  20. 20.
    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). Scholar
  21. 21.
    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)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Muhammad Adeel Azam
    • 1
    Email author
  • Khan Bahadar Khan
    • 2
  • Muhammad Aqeel
    • 1
  • Abdul Rehman Chishti
    • 2
  • Muhammad Nawaz Abbasi
    • 2
  1. 1.Department of Electronic EngineeringUCET, The Islamia University of BahawalpurBahawalpurPakistan
  2. 2.Department of Telecommunication EngineeringUCET, The Islamia University of BahawalpurBahawalpurPakistan

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