Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Image Management for Biological Data

  • Arnab Bhattacharya
  • Vebjorn Ljosa
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_629

Synonyms

Databases for biomedical images; Image management for life sciences

Definition

Image management for biological data refers to the organization of biological images and their associated metadata and annotations in a digital system so that they can be searched, retrieved and shared.

Historical Background

The need to manage digital micrographs stored in a computer arose in the early 1990s, when digital cameras started to replace film cameras. Rapid improvement in microscopy technology coupled with steady decline in prices led to an unprecedented increase in the collection of digital biomedical images. As a result, the requirement for systems to organize, search and share the images became clear. At the same time, the successes of large scale acquisition and analysis of genomic and gene expression data have prompted an increased interest in large-scale image analysis and the use of statistical methods to find patterns in large image sets. The ability of images to capture spatial...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    An Y, Borgida A, Miller RJ, Mylopoulos J. A semantic approach to discovering schema mapping expressions. In: Proceedings of the 23rd International Conference on Data Engineering; 2007. p. 206–15.Google Scholar
  2. 2.
    Carazo JM, Stelzer EH, Engel A, Fita I, Henn C, Machtynger J, McNeil P, Shotton DM, Chagoyen M, de Alarcón PA, Fritsch R, Heymann JB, Kalko S, Pittet JJ, Rodriguez-Tomé P, Boudier T. Organising multi-dimensional biological image information: the BioImage database. Nucleic Acids Res. 1999;27(1):280–3.CrossRefGoogle Scholar
  3. 3.
    Datta R, Li J, Wang JZ. Content-based image retrieval: approaches and trends of the New Age. Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval; 2005. p. 253–62.Google Scholar
  4. 4.
    Fowlkes CC, Hendriks CLL, Keränen SVE, Biggin MD, Knowles DW, Sudar D, Malik J. Registering Drosophila embryos at cellular resolution to build a quantitative 3D atlas of gene expression patterns and morphology. In: Proceedings of the International Workshop on Bioimage Data Mining and Informatics, IEEE Computational Systems Bioinformatics Conference; 2005.Google Scholar
  5. 5.
    Gonzalez-Couto E, Hayes B, Danckaert A. The life sciences global image database (GID). Nucleic Acids Res. 2001;29(1):336–9.CrossRefGoogle Scholar
  6. 6.
    Ljosa V, Bhattacharya A, Singh AK. Indexing spatially sensitive distance measures using multi-resolution lower bounds. In: Advances in Database Technology, Proceedings of the 10th International Conference on Extending Database Technology; 2006. p. 865–83.Google Scholar
  7. 7.
    Ljosa V, Singh AK. APLA: indexing arbitrary probability distributions. In: Proceedings of the 23rd International Conference on Data Engineering; 2007. p. 946–55.Google Scholar
  8. 8.
    Loo L-H, Wu LF, Altschuler SJ. Image-based multivariate profiling of drug responses from single cells. Nat Methods. 2007;4(5):445–53.Google Scholar
  9. 9.
    Martone ME, Zhang S, Gupta A, Qian X, He H, Price DL, Wong M, Santini S, Ellisman MH. The cell-centered database: a database for multiscale structural and protein localization data from light and electron microscopy. Neuroinformatics. 2003;1(4):379–95.CrossRefGoogle Scholar
  10. 10.
    Moffat J, Grueneberg DA, Yang X, Kim SY, Kloepfer AM, Hinkle G, Piqani B, Eisenhaure TM, Luo B, Grenier JK, Carpenter AE, Foo SY, Stewart SA, Stockwell BR, Hacohen N, Hahn WC, Lander ES, Sabatini DM, Root DE. A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell. 2006;124(6):1283–98.CrossRefGoogle Scholar
  11. 11.
    Sarma AD, Benjelloun O, Halevy A, Widom J. Working models for uncertain data. In: Proceedings of the 22nd International Conference on Data Engineering; 2006.Google Scholar
  12. 12.
    Stonebraker M, Abadi DJ, Batkin A, Chen X, Cherniack M, Ferreira M, Lau E, Lin A, Madden S, O’Neil E, O’Neil P, Rasin A, Tran N, Zdonik S. C-store: a column-oriented DBMS. In: Proceedings of the 31st International Conference on Very Large Data Bases; 2005. p. 553–64.Google Scholar
  13. 13.
    Swedlow JR, Goldberg I, Brauner E, Sorger PK. Informatics and quantitative analysis in biological imaging. Science. 2003;300(5616):100–2.CrossRefGoogle Scholar
  14. 14.
    Toga AW. Neuroimage databases: the good, the bad and the ugly. Nat Rev Neurosci. 2002;3(4):302–9.CrossRefGoogle Scholar
  15. 15.
    Zhou XS, Huang TS. Relevance feedback in image retrieval: a comprehensive review. Multimedia Systems. 2003;8(6):536–44.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Indian Institute of TechnologyKanpurIndia
  2. 2.Broad Institute of MIT and HarvardCambridgeUSA

Section editors and affiliations

  • Louiqa Raschid
    • 1
  1. 1.Robert H. Smith School of BusinessUniversity of MarylandCollege ParkUSA