Processing algorithms for tracking speckle shifts in optical elastography of biological tissues.

Parametric and nonparametric data processing schemes for analyzing translating laser speckle data used to investigate the mechanical behavior of biological tissues are examined. Cross-correlation, minimum mean square estimator, maximum likelihood, and maximum entropy approaches are discussed and compared on speckle data derived from cortical bone samples undergoing dynamic loading. While it was not the purpose of this paper to demonstrate that one processing technique is superior to another, maximum likelihood and maximum entropy approaches are shown to be particularly useful when the observed speckle motion is small.

[1]  C. Wykes,et al.  Holographic and Speckle Interferometry: A Discussion of the Theory, Practice and Application of the Techniques , 1983 .

[2]  Jc Dainty,et al.  PROBABILITY, STATISTICAL OPTICS AND DATA TESTING - A PROBLEM-SOLVING APPROACH - FRIEDEN,BR , 1983 .

[3]  S J Kirkpatrick,et al.  Micromechanical behavior of cortical bone as inferred from laser speckle data. , 1998, Journal of biomedical materials research.

[4]  Bernd Jähne,et al.  Practical handbook on image processing for scientific applications , 1997 .

[5]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[6]  Ichirou Yamaguchi.  Simplified Laser-Speckle Strain Gauge , 1982 .

[7]  S J Kirkpatrick,et al.  Transform method of processing for speckle strain-rate measurements. , 1994, Applied optics.

[8]  G. Tribillon,et al.  Biological Activity Measurement on Botanical Specimen Surfaces Using a Temporal Decorrelation Effect of Laser Speckle , 1989 .

[9]  Ichirou Yamaguchi.  Advances in the laser speckle strain gauge , 1988 .

[10]  Pramod Rastogi,et al.  Optical measurement techniques and applications , 1997 .

[11]  E. Parzen.  1. Random Variables and Stochastic Processes , 1999 .

[12]  Ichirou Yamaguchi,et al.  Resolution improvement in speckle displacement and strain sensor by correlation interpolation , 1992, Other Conferences.

[13]  Gary Cloud,et al.  Optical Methods of Engineering Analysis , 1996 .

[14]  A. Robert,et al.  HAUGEN, . The New Finance: The Case against Efficient Markets (Englewood Prentice Hall. , 1995 .

[15]  Raja Muthupillai,et al.  Magnetic resonance imaging of transverse acoustic strain waves , 1996, Magnetic resonance in medicine.

[16]  B. Frieden.  Probability, Statistical Optics and Data Testing: A Problem Solving Approach , 2001 .

[17]  S J Kirkpatrick,et al.  High resolution imaged laser speckle strain gauge for vascular applications. , 2000, Journal of biomedical optics.

[18]  R. Bracewell.  Two-dimensional imaging , 1994 .

[19]  Ichirou Yamaguchi,et al.  Speckle Displacement and Decorrelation in the Diffraction and Image Fields for Small Object Deformation , 1981 .

[20]  B. Welsh,et al.  Imaging Through Turbulence , 1996 .

[21]  S. L. Harris,et al.  Applied Numerical Methods for Engineers Using MATLAB and C , 1999 .

[22]  Harris A. Schilling,et al.  Applied Numerical Methods for Engineers Using MATLAB , 1999 .

[23]  James S. Walker.  Fast Fourier Transforms , 1991 .

[24]  J. Ophir,et al.  Elastography: A Quantitative Method for Imaging the Elasticity of Biological Tissues , 1991, Ultrasonic imaging.

[25]  L. Wilson,et al.  Spectral tissue strain: a new technique for imaging tissue strain using intravascular ultrasound. , 1994, Ultrasound in medicine & biology.

[26]  Donald D. Duncan,et al.  New speckle technique for noncontact measurement of small creep rates , 1992 .

[27]  K. Parker,et al.  "Sonoelasticity" images derived from ultrasound signals in mechanically vibrated tissues. , 1990, Ultrasound in medicine & biology.