Objective Morphological Quantification of Microscopic Images Using a Fast Fourier Transform (FFT) Analysis

Samuel E. Taylor1, Tuoxin Cao1, Pooja M. Talauliker1, Jonathan Lifshitz1

1 University of Kentucky College of Medicine, Lexington, Kentucky
Publication Name:  Current Protocols Essential Laboratory Techniques
Unit Number:  Unit 9.5
DOI:  10.1002/9780470089941.et0905s07
Online Posting Date:  October, 2013
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Abstract

Quantification of immunohistochemistry (IHC) and immunofluorescence (IF) using image intensity depends on a number of variables. These variables add a subjective complexity in keeping a standard within and between laboratories. Fast Fourier Transformation (FFT) algorithms, however, allow for a rapid and objective quantification (via statistical analysis) using cell morphologies when the microscopic structures are oriented or aligned. Quantification of alignment is given in terms of a ratio of FFT intensity to the intensity of an orthogonal angle, giving a numerical value of the alignment of the microscopic structures. This allows for a more objective analysis than alternative approaches, which rely upon relative intensities. Curr. Protoc. Essential Lab. Tech. 7:9.5.1‐9.5.12. © 2013 by John Wiley & Sons, Inc.

Keywords: quantification; microscopy; fast Fourier transformation; immunohistochemistry

     
 
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Table of Contents

  • Overview and Principles
  • Protocols
  • Basic Protocol 1: Preparation of Images for FFT Analysis
  • Basic Protocol 2: FFT Analysis of Images
  • Commentary
  • Literature Cited
  • Figures
  • Tables
     
 
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Materials

Basic Protocol 1: Preparation of Images for FFT Analysis

  Materials
  • Histological sections on microscope slides (e.g. brain sections as described in van Bregt et al., )
  • Research microscope with attached digital camera
  • Image processing software (e.g., Adobe Photoshop)

Basic Protocol 2: FFT Analysis of Images

  Materials
  • NIH Image J software with the “Oval profile Plot” plug‐in (author, Bill O'Connell, http://rsb.info.nih.gov/ij/plugins/oval‐profile.html)
  • Spreadsheet calculation software (e.g., Microsoft Excel)
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Figures

Videos

Literature Cited

Literature Cited
  Alexander, J.K., Fuss, B., and Colello, R.J. 2006. Electric field‐induced astrocyte alignment directs neurite outgrowth. Neuron Glia Biol. 2:93‐103.
  Ayres, C.E., Bowlin, G.L., Henderson, S.C., Taylor, L., Schultz, J., Alexander, J., Telemeco, T.A. and Simpson, D.G., 2006. Modulation of anisotropy in electrospun tissue‐engineering scaffolds: Analysis of fiber alignment by the fast Fourier transform. Biomaterials27:5524‐5534.
  Ayres, C.E., Bowlin, G.L., Pizinger, R., Taylor, L.T., Keen, C.A., and Simpson, D.G., 2007. Incremental changes in anisotropy induce incremental changes in the material properties of electrospun scaffolds. Acta Biomaterialia3:651‐661.
  Ayres, C.E., Jha, B.S., Meredith, H., Bowman, J.R., Bowlin, G.L., Henderson, S.C., and Simpson, D.G. 2008. Measuring fiber alignment in electrospun scaffolds: A user's guide to the 2D fast Fourier transform approach. J. Biomater. Sci. Polym. Ed.19:603‐621.
  Tonar, Z., Němeček, S., Holota, R., Kočová, J., Třeška, V., Moláček, J., and Tomáš Kohoutek, H. 2003. Microscopic image analysis of elastin network in samples of normal, athersclerotic and aneurysmatic abdominal aorta and its biomechanical implications. J. Appl. Biomed.1:149‐159.
  Valmikinathan, C.M., Tian, J., Wang, J., and Yu, X. 2008. Novel nanofibrous spiral scaffolds for neural tissue engineering. J. Neural Eng.5:422.
  van Bregt, D.R., Thomas, T.C., Hinzman, J.M., Cao, T., Liu, M., Bing, G., Gerhardt, G.A., Pauly, J.R., and Lifshitz, J. 2012. Substantia nigra vulnerability after a single moderate diffuse brain injury in the rat. Exp. Neurol.1:8‐19.
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Supplementary Material