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A Simplified Implementation of Edge Detection in MATLAB is Faster and More Sensitive than Fast Fourier Transform for Actin Fiber Alignment Quantification

Published online by Cambridge University Press:  09 March 2011

Steven Frank Kemeny  and
Alisa Morss Clyne
Steven Frank Kemeny
Affiliation:
Drexel University, Mechanical Engineering and Mechanics, Bossone Research Center, Philadelphia, PA 19104, USA
Alisa Morss Clyne*
Affiliation:
Drexel University, Mechanical Engineering and Mechanics, Alumni Labs, Philadelphia, PA 19104,USA
*
Corresponding author. E-mail: alisam@coe.drexel.edu
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Abstract

Fiber alignment plays a critical role in the structure and function of cells and tissues. While fiber alignment quantification is important to experimental analysis and several different methods for quantifying fiber alignment exist, many studies focus on qualitative rather than quantitative analysis perhaps due to the complexity of current fiber alignment methods. Speed and sensitivity were compared in edge detection and fast Fourier transform (FFT) for measuring actin fiber alignment in cells exposed to shear stress. While edge detection using matrix multiplication was consistently more sensitive than FFT, image processing time was significantly longer. However, when MATLAB functions were used to implement edge detection, MATLAB's efficient element-by-element calculations and fast filtering techniques reduced computation cost 100 times compared to the matrix multiplication edge detection method. The new computation time was comparable to the FFT method, and MATLAB edge detection produced well-distributed fiber angle distributions that statistically distinguished aligned and unaligned fibers in half as many sample images. When the FFT sensitivity was improved by dividing images into smaller subsections, processing time grew larger than the time required for MATLAB edge detection. Implementation of edge detection in MATLAB is simpler, faster, and more sensitive than FFT for fiber alignment quantification.

Keywords

endotheliumshear stressimage processingedge detectionMATLABactin fibers
Type
Biological Applications
Information
Microscopy and Microanalysis , Volume 17 , Issue 2 , April 2011 , pp. 156 - 166
Copyright
Copyright © Microscopy Society of America 2011

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References

REFERENCES

Barbee, K.A., Mundel, T., Lal, R. & Davies, P.F. (1995). Subcellular distribution of shear stress at the surface of flow-aligned and nonaligned endothelial monolayers. Am J Physiol Heart Circ Physiol 268(4), H1765–1772.CrossRefGoogle ScholarPubMed
Chaudhuri, B., Kundu, P. & Sarkar, N. (1993). Detection and gradation of oriented texture. Pattern Recogn Lett 14(2), 147–153.CrossRefGoogle Scholar
Chaudhuri, S., Nguyen, H., Rangayyan, R.M., Walsh, S. & Frank, C.B. (1987). A Fourier domain directional filterng method for analysis of collagen alignment in ligaments. IEEE Trans Biomed Eng 34(7), 509–518.CrossRefGoogle ScholarPubMed
David, M.W.A. (1989). Two-dimensional finite impulse repsonse filters. U.S. Patent 4,821,223, April 11, 1989.Google Scholar
Davies, P.F., Dewey, C.F. Jr., Bussolari, S.R., Gordon, E.J. & Gimbrone, M.A. Jr. (1984). Influence of hemodynamic forces on vascular endothelial function. In vitro studies of shear stress and pinocytosis in bovine aortic cells. J Clin Invest 73(4), 1121–1129.CrossRefGoogle ScholarPubMed
DeMeester, S.L., Cobb, J.P., Hotchkiss, R.S., Osborne, D.F., Karl, I.E., Tinsley, K.W. & Buchman, T.G. (1998). Stress-induced fractal rearrangement of the endothelial cell cytoskeleton causes apoptosis. Surgery 124(2), 362–371.CrossRefGoogle ScholarPubMed
Dewey, C.F. Jr., Bussolari, S.R., Gimbrone, M.A. Jr. & Davies, P.F. (1981). The dynamic response of vascular endothelial cells to fluid shear stress. J Biomechan Eng 103(3), 177–185.CrossRefGoogle ScholarPubMed
Dixelius, J., Cross, M., Matsumoto, T., Sasaki, T., Timpl, R. & Claesson-Welsh, L. (2002). Endostatin regulates endothelial cell adhesion and cytoskeletal organization. Cancer Res 62(7), 1944–1947.Google ScholarPubMed
Duda, R.O. & Hart, P.E. (1973). Pattern Classification and Scene Analysis. New York: John Wiley & Sons, Inc.Google Scholar
Eskin, S.G., Ives, C.L., McIntire, L.V. & Navarro, L.T. (1984). Response of cultured endothelial cells to steady flow. Microvasc Res 28(1), 87–94.CrossRefGoogle ScholarPubMed
Flaherty, J.T., Pierce, J.E., Ferrans, V.J., Patel, D.J., Tucker, W.K. & Fry, D.L. (1972). Endothelial nuclear patterns in the canine arterial tree with particular reference to hemodynamic events. Circ Res 30(1), 23–33.CrossRefGoogle ScholarPubMed
Fuseler, J.W., Millette, C.F., Davis, J.M. & Carver, W. (2007). Fractal and image analysis of morphological changes in the actin cytoskeleton of neonatal cardiac fibroblasts in response to mechanical stretch. Microsc Microanal 13(2), 133–143.CrossRefGoogle ScholarPubMed
Galbraith, C.G., Skalak, R. & Chien, S. (1998). Shear stress induces spatial reorganization of the endothelial cell cytoskeleton. Cell Motil Cytoskel 40, 317–330.3.0.CO;2-8>CrossRefGoogle ScholarPubMed
Ganesan, L. & Bhattacharyya, P. (1997). Edge detection in untextured and textured images-a common computational framework. IEEE Trans Syst Man Cy B 27(5), 823–834.CrossRefGoogle ScholarPubMed
Girard, P.R. & Nerem, R.M. (1995). Shear stress modulates endothelial cell morphology and F-actin organization through the regulation of focal adhesion-associated proteins. J Cell Physiol 163(1), 179–193.CrossRefGoogle ScholarPubMed
Gonzalez, R.C. & Wintz, P. (1977). Digital Image Processing. Reading, MA: Addison-Wesley Publishing Company, Inc.Google Scholar
Karlon, W.J., Hsu, P.-P., Li, S., Chien, S., McCulloch, A.D. & Omens, J.H. (1999). Measurement of orientation and distribution of cellular alignment and cytoskeletal organization. Ann Biomed Eng 27(6), 712–720.CrossRefGoogle ScholarPubMed
Kaunas, R., Nguyen, P., Usami, S. & Chien, S. (2005). Cooperative effects of Rho and mechanical stretch on stress fiber organization. PNAS 102(44), 15895–15900.CrossRefGoogle ScholarPubMed
Kaunas, R., Usami, S. & Chien, S. (2006). Regulation of stretch-induced JNK activation by stress fiber orientation. Cell Signal 18(11), 1924–1931.CrossRefGoogle ScholarPubMed
Kim, A., Lakshman, N. & Petroll, W.M. (2006). Quantitative assessment of local collagen matrix remodeling in 3-D culture: The role of Rho kinase. Exp Cell Res 312(18), 3683–3692.CrossRefGoogle ScholarPubMed
Knight, M., Idowu, B., Lee, D. & Bader, D. (2001). Temporal changes in cytoskeletal organisation within isolated chondrocytes quantified using a novel image analysis technique. Med Biol Eng Comput 39(3), 397–404.CrossRefGoogle ScholarPubMed
Leemreis, J.R., Versteilen, A.M.G., Sipkema, P., Groeneveld, A.B.J. & Musters, R.J.P. (2006). Digital image analysis of cytoskeletal F-actin disintegration in renal microvascular endothelium following ischemia/reperfusion. Cytom A 69(9), 973–978.CrossRefGoogle ScholarPubMed
Marquez, J.P. (2006). Fourier analysis and automated measurement of cell and fiber angular orientation distributions. Int J Solids Struct 43(21), 6413–6423.CrossRefGoogle Scholar
Marr, D. & Hildreth, E. (1980). Theory of edge detection. P R Soc Lond B Bio 207(1167), 187–217.Google ScholarPubMed
Masters, B.R. (2004). Fractual analysis of the vascular tree in the human retina. Ann Rev Biomed Eng 6(1), 427–452.CrossRefGoogle Scholar
Ng, C.P., Hinz, B. & Swartz, M.A. (2005). Interstitial fluid flow induces myofibroblast differentiation and collagen alignment in vitro. J Cell Sci 118(20), 4731–4739.CrossRefGoogle ScholarPubMed
Nishimura, T. & Ansell, M.P. (2002). Fast Fourier transform and filtered image analyses of fiber orientation in OSB. Wood Sci Technol 36(4), 287–307.CrossRefGoogle Scholar
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cy 9(1), 62–66.CrossRefGoogle Scholar
Palmer, B. & Bizios, R. (1997). Quantitative characterization of vascular endothelial cell morphology and orientation using Fourier transform analysis. J Biomechan Eng 119, 159–165.CrossRefGoogle ScholarPubMed
Pearlman, E., Weber, K.T., Janicki, J.S., Pietra, G.G. & Fishman, A.P. (1982). Muscle fiber orientation and connective tissue content in the hypertrophied human heart. Lab Invest 46(2), 158–164.Google ScholarPubMed
Petroll, W., Cavanagh, H., Barry, P., Andrews, P. & Jester, J. (1993). Quantitative analysis of stress fiber orientation during corneal wound contraction. J Cell Sci 104(2), 353–363.CrossRefGoogle ScholarPubMed
Pourdeyhimi, B., Ramanathan, R. & Dent, R. (1996). Measuring fiber orientation in nonwovens: Part I: Simulation. Textile Res J 66(11), 713–722.CrossRefGoogle Scholar
Rabiner, L. (1972). Linear program design of finite impulse response (FIR) digital filters. IEEE Trans Audio Electroacoust 20(4), 280–288.CrossRefGoogle Scholar
Sahai, E., Olson, M.F. & Marshall, C.J. (2001). Cross-talk between Ras and Rho signalling pathways in transformation favours proliferation and increased motility. EMBO J 20(4), 755–766.CrossRefGoogle ScholarPubMed
Sobel, I. & Feldman, J.A. (1968). A 3×3 isotropic gradient operator for image processing. In Stanford Artificial Project. Stanford, CA: Stanford University.Google Scholar
Thomason, D.B., Anderson, O. III & Menon, V. (1996). Fractal analysis of cytoskeleton rearrangement in cardiac muscle during head-down tilt. J Appl Physiol 81(4), 1522–1527.CrossRefGoogle ScholarPubMed
Vartanian, K.B., Kirkpatrick, S.J., Hanson, S.R. & Hinds, M.T. (2008). Endothelial cell cytoskeletal alignment independent of fluid shear stress on micropatterned surfaces. Biochem Biophys Res Comm 371(4), 787–792.CrossRefGoogle ScholarPubMed
Versari, S., Villa, A., Bradamante, S. & Maier, J.A.M. (2007). Alterations of the actin cytoskeleton and increased nitric oxide synthesis are common features in human primary endothelial cell response to changes in gravity. Biochim Biophys Acta MolCell Res 1773(11), 1645–1652.CrossRefGoogle ScholarPubMed
Yang, C.-F., Crosby, C.M., Eusufzai, A.R.K. & Mark, R.E. (1987). Determination of paper sheet fiber orientation distributions by a laser optical diffraction method. J Appl Polym Sci 34(3), 1145–1157.CrossRefGoogle Scholar
Yoshigi, M., Clark, E.B. & Yost, H.J. (2003). Quantification of stretch-induced cytoskeletal remodeling in vascular endothelial cells by image processing. Cytom A 55A(2), 109–118.CrossRefGoogle Scholar
Yurchenco, P. & Schittny, J. (1990). Molecular architecture of basement membranes. FASEB J 4(6), 1577–1590.CrossRefGoogle ScholarPubMed
Zhou, Y. & Zheng, Y.-P. (2008). Estimation of muscle fiber orientation in ultrasound images using revoting hough transform (RVHT). Ultrasound Med Biol 34(9), 1474–1481.CrossRefGoogle ScholarPubMed
Zuijlen, P.P.v., Vries, H.J.d., Lamme, E.N., Coppens, J.E., Marle, J.v., Kreis, R.W. & Middelkoop, E. (2002). Morphometry of dermal collagen orientation by Fourier analysis is superior to multi-observer assessment. J Pathol 198(3), 284–291.CrossRefGoogle ScholarPubMed