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Basic Image Analysis and Manipulation in ImageJ
Publication Name:
Current Protocols in Molecular Biology
Unit Number:
Unit 14.15
DOI:
10.1002/0471142727.mb1415s102
Online Posting Date:
April, 2013
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
Materials
Basic Protocol Materials
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Figures
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Figure 14.15.1 ImageJ (upper) and Fiji (lower) interface running in Mac OS X. The ImageJ and Fiji main windows contain the toolbar (in Windows and Linux the menubar is contained at the top of this window). The ImageJ toolbar contains tools for making selections (e.g., Lasso), drawings, zooming and scrolling, etc. The status bar is located directly below the toolbar. When the cursor is over an image, the status bar displays pixel intensities and coordinates. The status bar also displays memory in use, memory available, and percent memory. As selections are created or resized, selection properties (e.g., location, width, etc.) are displayed on the status bar. -
Figure 14.15.2 Image display and contrast stretching. After an image is loaded, slider bars in the B&C window can be used to interactively alter the brightness and contrast of the active image. The histogram at the top of the window captures the distribution of pixel intensities in the image, while the overlapping line graph shows how pixel values map to the range of values present in the image. The two numbers under the plot are the minimum and maximum displayed pixel values, which define the display range. The Brightness slider modulates image brightness by shifting the display range. The Contrast slider increases or decreases contrast by varying the width of the display range. The narrower the display range, the higher the contrast. -
Figure 14.15.3 The freehand selection tool (see Lasso). The freehand selection tool uses the cursor to create a user-defined region of interest. Shown are HeLa nuclei stained with 4′6-diamidino-2-phenylindole (DAPI) to identify nuclei. Microscopy was performed at 20× magnification. -
Figure 14.15.4 A sample segmentation and feature extraction example in ImageJ. (A) Thresholding was used to distinguish foreground and background pixels. The result is all pixels either set to black (foreground) or white (backround) to create a binary image. (B) The Analyze Particles command was used to count and measure properties (features) associated with the binary image. A report was generated (lower right), which indicates the number of objects identified, object area, and mean pixel value associated with each object. -
Figure 14.15.5 Unmixing multispectral images using the PoissinNMF plug-in. (A) The input into the plug-in is a grayscale stack, with each slice in the stack representing a different spectral channel. The grayscale stack can be from a plain RGB image using the methods described in the Approximating the Optical Density/Absorbance in 8-Bit Color Brightfield Images section. (B) Once the plug-in is initiated, various unmixing parameters can be manually assigned. (C) When the unmixing routine is executed on the input image, progress windows pop up showing the spectra of the dyes in the image. (D) Images are displayed as grayscale stacks, with the number of slices matching the user-set number of sources.
Videos
Literature Cited
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