IICBU 2008: a proposed benchmark suite for biological image analysis.
New technology for automated biological image acquisition has introduced the need for effective biological image analysis methods. These algorithms are constantly being developed by pattern recognition and machine vision experts, who tailor general computer vision techniques to the specific needs of...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 46; no. 9; pp. 943 - 948 |
|---|---|
| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2008
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105552633&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105552633 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2008 vid: 46 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105552633 NLM18668273 2010025082 10.1007/s11517-008-0380-5 NLM18668273 PMC2562655 105552633 ppf: 943 ppct: 5 formats: fmt: @attributes: type: P tig: atl: IICBU 2008: a proposed benchmark suite for biological image analysis. aug: au: Shamir L Orlov N Mark Eckley D Macura TJ Goldberg IG Shamir, Lior Orlov, Nikita Mark Eckley, David Macura, Tomasz J Goldberg, Ilya G affil: Laboratory of Genetics, Image Informatics and Computational Biology Unit, NIA/NIH, 333 Cassell Dr, Baltimore, MD 21224, USA sug: subj: Benchmarking Image Processing, Computer Assisted Standards Resource Databases Algorithms Animals Image Processing, Computer Assisted Methods Internet ab: New technology for automated biological image acquisition has introduced the need for effective biological image analysis methods. These algorithms are constantly being developed by pattern recognition and machine vision experts, who tailor general computer vision techniques to the specific needs of biological imaging. However, computer scientists do not always have access to biological image datasets that can be used for computer vision research, and biologist collaborators who can assist in defining the biological questions are not always available. Here, we propose a publicly available benchmark suite of biological image datasets that can be used by machine vision experts for developing and evaluating biological image analysis methods. The suite represents a set of practical real-life imaging problems in biology, and offers examples of organelles, cells and tissues, imaged at different magnifications and different contrast techniques. All datasets are available for free download at http://ome.grc.nia.nih.gov/iicbu2008 . pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|