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Digital Image Processing and Analysis with ImageJ

Sean R. Gallagher¹

¹UVP, LLC, Upland, California

Publication Name:

Current Protocols Essential Laboratory Techniques

Unit Number:

Appendix 3C

DOI:

10.1002/9780470089941.eta03cs03

Online Posting Date:

June, 2010

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Abstract

ImageJ is a freely available, cross-platform (e.g., Windows, Mac, Linux) image processing and analysis program developed by the NIH. In addition to being readily available for no cost, ImageJ is supported by a wide range of constantly evolving user-created functionalities to address a remarkable range of applications, complementing commercial software that typically comes with imaging instruments such as digital gel-imaging systems or microscopy workstations. New processing/analysis macros and plug-ins are routinely added to the support site, and are frequently validated via refereed publications. With the continued improvements and growth of fluorescence-based applications, ImageJ continues to be a mainstay in the laboratory. ImageJ has extensive support materials available online, its base code is regularly updated, and a survey of Medline references indicates that it is one of the most widely used image-analysis packages available today. Curr. Protoc. Essential Lab. Tech. 3:A.3C.1-A.3C.24. © 2010 by John Wiley & Sons, Inc.

Keywords: computer graphics; image enhancement; image interpretation; imaging; microscopy; software; electrophoresis; gel analysis; fluorescence-based imaging; digital imaging; protein blotting

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Overview and Principles
Strategic Planning
Protocols
Basic Protocol: Visualization and Quantitation with ImageJ
Alternate Protocol: Quantitation of Bands with Area Selection
Support Protocol: Using Pseudo Colors to Differentiate Two Separate Fluorescent Signals on a Single Protein Blot
Understanding Results
Troubleshooting
Selected Plug-Ins for ImageJ
Literature Cited
Figures
Tables

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Materials

Basic Protocol: Visualization and Quantitation with ImageJ

Materials

Windows-based computer (note that MacOS X and Linux are also supported)
Network and Internet connection (along with any needed passwords)
Dual monitors, one as the work area and the other as the marquee for the menu area (alternatively, use a larger monitor >24 in.)
Image acquired from digital imaging system such as a gel electrophoresis imager or a digital imaging microscopy workstation

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Figures

Figure A.3C.1

ImageJ menu and toolbar.

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Figure A.3C.2

Fluorescent protein blot showing fluorescently tagged protein standards and dual labeling of the proteins via IR680 and IR800 fluorescent antibody tags. The blot was prepared by separating proteins and immunoblotting according to unit 8.3. Panel (A) illustrates the location of the Adjust < Brightness/Contrast menu items to adjust visual appearance of the fluorescent protein blot. In panel (B) the Brightness and Contrast (B&C) window is adjusted with the brightness slider to give a better view of the blot and lane detail. In some cases, the background must be adjusted in order to identify the edges of a band or an area of interest that will be later quantitated. Also, when creating composites of pseudo-colored gels, a low background is required so that the colors for the individual molecules (proteins in this case) stand out.

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Figure A.3C.3

Basic blot analysis: creating a lane profile and determining the intensity of the identified band. The same procedure applies to proteins separated by gel electrophoresis. Panel (A) shows the selection of the first lane on the far left of the image to start the process. Alternatively, one can use Ctrl+1. Moving the rectangle to the next lane to the right and pressing Ctrl+2 (or choosing Next Lane) selects the next lane over, and by repeating the same process across the gel one can identify and highlight every lane as shown in panel (B). In panel B, all six lanes are highlighted and labeled with a rectangle selection prior to measuring the intensity of the bands. By selecting the Plot Lanes menu item, plot profiles for each lane are generated. Each one of the lanes is highlighted with an intensity profile of the bands identified in the lane. Panel (C) shows a plot of the selected band from lane 4. By plotting the lanes and then using the line tool to draw a line across the bottom of the selected bands (panel C), the area of each band can be determined with the background (i.e, the area below the drawn line) removed. Panel (D) shows a plot with area percentage noted for lane 4.Panel (E) shows the total intensity and percent results table for the highlighted bands in lanes 2, 3, 4, 5, and 6.

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Figure A.3C.4

Basic blot analysis using Region of Interest or ROI tools. Panel (A) shows the selection of the ROI Manager under the menu item Analyze. The ROI Manager lists the areas selected by the rectangle tool, and tabulates the data from those selections. In panel (B), five selected areas including an additional area 6 for the background were selected. The data show the mean and total intensity values, as well as the minimum and the maximum intensities. By subtracting the total background value (selection 6) from the total band intensity, the intensity of the bands can be determined accurately.

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Figure A.3C.5

Pseudo-coloring and combination of two separate images into a single blot. Panel (A) shows two identically sized images of the same blot with fluorescent bands identified by protein blotting—the left blot fluorescing at 680 nm and the right one at 800 nm. Panel (B) shows the Image menu item and the selection of Color and Merge Channels; the two images can be combined into a single image pseudo-colored in red and green. Panel (C) shows the selection of the red and green channels to indicate which bands will take on the pseudo-color red and green channels. Panel (D) illustrates the final combined blot showing the two separately imaged blots combined onto a single image.

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Literature Cited

Literature Cited
	Burger, W. and Burge, M. 2008. Digital Image Processing: An Algorithmic Introduction Using Java. Springer, New York.
	Burger, W. and Burge, M. 2009a. Principles of Digital Image Processing: Fundamental Techniques. Springer, London.
	Burger, W. and Burge, M. 2009b. Principles of Digital Image Processing: Core Algorithms. Springer, London.
	Collins, T.J. 2007. ImageJ for microscopy. BioTechniques 43:25-30.
	Ferreira, T.A. and Rasband, W. 2010. The ImageJ User Guide Version 1.43. http://rsbweb.nih.gov/ij/docs/user-guide.pdf.
Key Reference
	Ferreira and Rasband, 2010. See above.
Recently updated, this is an excellent overview and instruction to the use of ImageJ.
Internet Resources
	http://rsbweb.nih.gov/ij/
The main ImageJ site at the NIH, visited over 6,000,000 times to date. Contains documentation and links on ImageJ.
	http://imagejdocu.tudor.lu/
The ImageJ Wiki is a wealth of information in the user community, with HowTos, plug-ins, video tutorials, etc.
	http://imagejdocu.tudor.lu/doku.php id=video:beginner_help:imagej_beginner_s_tutorial
One of several video tutorials for ImageJ.