Using Google Reverse Image Search to Decipher Biological Images. Issue 1 (15th February 2018)

• Record Type: Journal Article
• Authors: Mamrosh, Jennifer L.; Moore, David D.
• Editors: Ausubel, Frederick M.; Brent, Roger; Kingston, Robert E.; Moore, David D.; Seidman, J.G.; Smith, John A.; Struhl, Kevin
• Is Part Of: Current protocols in molecular biology. Volume 111:Issue 1(2015)
• Page Start: 19.13.1
• Page End: 19.13.4
• Publication Date: 2018-02-15
• Subjects: image analysis -- reverse image search -- unknown mechanism; Molecular biology -- Technique -- Periodicals; Molecular biology -- Laboratory manuals; Molecular Biology -- methods; Biologie moléculaire -- Technique; Biologie moléculaire -- Manuels de laboratoire; Molecular biology; Molecular biology -- Technique; Laboratory Manual; Electronic reference sources; Laboratory manuals; 572.8028
• DOI: 10.1002/0471142727.mb1913s111
• Languages: English
• ISSNs: 1934-3639

Abstract

Abstract: Despite the range of tasks performed by biological image‐processing software, current versions cannot find matches for the image in question among the huge range of biological images that exist in the literature and elsewhere on the Internet. Google's Reverse Image Search is designed for this, and it is a simple, yet powerful tool that can be applied to decipher the contents of biological images. For images that contain unfamiliar or unknown elements, for example, Reverse Image Search can identify similar features in published images. Here we describe general guidelines for using this freely available tool to search published images in National Center for Biotechnology Information's (NCBI's) image database. These guidelines can be applied to a variety of types of biological images, including immunohistochemistry and electron microscopy, to facilitate straightforward and rapid searches using Google's Reverse Image Search. © 2015 by John Wiley & Sons, Inc.