Basic Image Analysis and Manipulation in ImageJ

Sean M. Hartig1

1 Baylor College of Medicine, Houston, Texas
Publication Name:  Current Protocols in Molecular Biology
Unit Number:  Unit 14.15
DOI:  10.1002/0471142727.mb1415s102
Online Posting Date:  April, 2013
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Abstract

Image analysis methods have been developed to provide quantitative assessment of microscopy data. In this unit, basic aspects of image analysis are outlined, including software installation, data import, image processing functions, and analytical tools that can be used to extract information from microscopy data using ImageJ. Step‐by‐step protocols for analyzing objects in a fluorescence image and extracting information from two‐color tissue images collected by bright‐field microscopy are included. Curr. Protoc. Mol. Biol. 102:14.15.1–14.15.12. © 2013 by John Wiley & Sons, Inc.

Keywords: ImageJ; microscopy; image analysis; object identification; spectral unmixing

     
 
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Table of Contents

  • Commentary
  • Literature Cited
  • Figures
     
 
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Materials

Basic Protocol 1:

  Materials
  • Image files: TIFF, GIF, JPEG, DICOM, BMP, PNG, and FITS native formats can be opened in ImageJ without plug‐ins—lossless image formats (BMP, TIFF, PNG, GIF) are preferred for quantitative analysis; JPEGs are usually compressed in a way that does not include all of the original data and often contain spatial and/or chromatic artifacts not present in the raw data
  • Image types: The bit depth defines the number of gray levels for any image—8‐ and 16‐bit (unsigned) integer images consist of 256 (0 to 255) and 65,536 (0 to 65,535) gray levels, respectively; 32‐bit images use floating‐point numbers; for RGB color images, each pixel is assigned a specific intensity for each of the three‐color channels (red, green, blue); splitting 24‐bit RGB images generates three 8‐bit images (red, green, blue)
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Figures

Videos

Literature Cited

Literature Cited
   Carpenter, A.E., Jones, T.R., Lamprecht, M.R., Clarke, C., Kang, I.H., Friman, O., Guertin, D.A., Chang, J.H., Lindquist, R.A., Moffat, J., Golland, P., and Sabatini, D.M. 2006. CellProfiler: Image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7:R100.
   Jones, T.R., Kang, I.H., Wheeler, D.B., Lindquist, R.A., Papallo, A., Sabatini, D.M., Golland, P., and Carpenter, A.E. 2008. CellProfiler Analyst: Data exploration and analysis software for complex image‐based screens. BMC Bioinformatics 9:482.
   Neher, R.A., MItkovski, M., Kirchhoff, F., Neher, E., Theis, F.J., and Zeug, A. 2009. Blind source separation techniques for the decomposition of multiply labeled fluorescence images. Biophys. J. 96:3791‐3800.
   Pepperkok, R. and Ellenberg, J. 2006. High‐throughput fluorescence microscopy for systems biology. Nat. Rev. Mol. Cell Biol. 7:690‐696.
   Rajaram, S., Pavie, B., Wu, L.F., and Altschuler, S.J. 2012. PhenoRipper: Software for rapidly profiling microscopy images. Nat. Methods 9:635‐637.
   Uhlen, M., Bjorling, E., Agaton, C., Szigyarto, C.A., Amini, B., Andersen, E., Andersson, A.C., Angelidou, P., Asplund, A., Asplund, C., Berglund, L., Bergstrom, K., Brumer, H., Cerjan, D., Ekstrom, M., Elobeid, A., Eriksson, C., Fagerberg, L., Falk, R., Hansson, M., Hedhammar, M., Hercules, G., Kampf, C., Larsson, K., Lindskog, M., Lodewyckx, W., Lund, J., Lundeberg, J., Magnusson, K., Malm, E., Nilsson, P., Odling, J., Oksvold, P., Olsson, I., Oster, E., Ottosson, J., Paavilainen, L., Persson, A., Rimini, R., Rockberg, J., Runeson, M., Sivertsson, A., Skollermo, A., Steen, J., Stenvall, M., Sterky, F., Stromberg, S., Sundberg, M., Tegel, H., Tourle, S., Wahlund, E., Walden, A., Wan, J., Wernerus, H., Westberg, J., Wester, K., Wrethagen, U., Xu, L. L., Hober, S., and Ponten, F. 2005. A human protein atlas for normal and cancer tissues based on antibody proteomics. Mol. Cell. Proteomics 4:1920‐1932.
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