Fourier analysis and automated measurement of cell and fiber angular orientation distributions

Marquez, J. Pablo

doi:10.1016/j.ijsolstr.2005.11.003

Cited by 64 publications

(80 citation statements)

References 18 publications

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“…Other microstructural features, such as cells and curved fibers, were not considered in this study but have been examined with success in other studies that employed a Fourier transform method. 18,19 Our own experience using the FTM on ellipsoidal shapes has shown good agreement, but a thorough examination has not been conducted. Curved fibers will produce a signal at each angle band in the power spectrum in proportion to how much of the fiber is oriented at a given angle, producing effectively a mass-weighted orientation distribution.…”

Section: Discussionmentioning

confidence: 99%

Comparison of 2D fiber network orientation measurement methods

Sander

Barocas

2008

J Biomedical Materials Res

106

126

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The mechanical properties of tissues, tissue analogs, and biomaterials are dependent on their underlying microstructure. As such, many mechanical models incorporate some aspect of microstructure, but a robust protocol for characterizing fiber architecture remains a challenge. A number of image-based methods, including mean intercept length (MIL), line fraction deviation (LFD), and Fourier transform methods (FTM), have been applied to microstructural images to describe material heterogeneity and orientation, but a performance comparison, particularly for fiber networks, has not been conducted. In this study, we constructed 40 two-dimensional test images composed of simulated fiber networks varying in fiber number, orientation, and anisotropy index. We assessed the accuracy of each method in measuring principal direction (theta) and anisotropy index (alpha). FTM proved to be the superior method because it was more reliable in measurement accuracy (Deltatheta = 2.95 degrees +/- 6.72 degrees , Deltaalpha = 0.03 +/- 0.02), faster in execution time, and flexible in its application. MIL (Deltatheta = 6.23 degrees +/- 10.68 degrees , Deltaalpha = 0.08 +/- 0.06) was not significantly less accurate than FTM but was much slower. LFD (Deltatheta = 9.97 degrees +/- 11.82 degrees , Deltaalpha = 0.24 +/- 0.13) consistently underperformed. FTM results agreed qualitatively with fibrin gel SEM micrographs, suggesting that FTM can be used to obtain image-based statistical measurements of microstructure.

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Section: Discussionmentioning

confidence: 99%

Comparison of 2D fiber network orientation measurement methods

Sander

Barocas

2008

J Biomedical Materials Res

106

126

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“…However, processing error may be introduced due to many reasons such as truncated fibres, resolution of image, etc. The result can be improved by error minimizing techniques such as Von Mises distribution and by optimization techniques such as Monte Carlo simulations 48 .…”

Section: Applying Dft For Fibre Orientationmentioning

confidence: 99%

Extracting Cell and Fibre Orientation from Microscopic Images:Computational Methods and Tools

Sharma

2016

Current Science

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Orientation of cell and its actin fibres dictate various properties of the cell and its associated tissue including morphology, cell migration and response of cell to external stimuli. While the vital role of cell and actin fibre orientation has been demonstrated in various studies and across many cell-types, extracting this information from microscopy images is a challenging task. Although several methods have been developed to quantify alignment of a cell and its actin fibres from experimental images, all of them differ at one step or the other. We highlight the importance of orientation of cell and associated fibres and present a generic scheme of various approaches to assimilate the widely used methods. We have presented a systematic approach to determine cell and fibre orientation. The study will improve our understanding of the core processes and will also help in development of high throughput imaging methods to extract information from experimental images.Keywords: BEAS, cell orientation, DFT, fibre orientation, FFT, image processing, thresholding.IN the study of biological phenomena, cell is at a very fundamental level, as it is the smallest building block of all organisms. In fact, it is the smallest form of life that exhibits all the basic traits that form life such as birth, ingestion, digestion, protection and finally death. Cell during its life cycle experiences a number of mechanical interactions with surrounding environment necessary for its communication, proliferation, differentiation and migration [1][2][3][4][5][6][7][8] . A cell responds to these mechanical forces by exhibiting changes at various levels such as in morphology, protein synthesis 9,10 , gene expression 11,12 , etc. Then, cell tries to adapt to its environment through these changes. Several of its mechanical properties facilitate the adaptation of cell to the associated environment. Therefore, to understand the mechanism of working of a cell, knowledge of its mechanical properties and how do these get affected by a cell's surrounding environment is very vital. Microscopy-based imaging and its subsequent analysis is one of the traditional and widely used approaches to gain insights into the cell's mechanical and morphological properties. While microscopy imaging is a widely used approach to extract quantitative information about cell morphology, image analysis and morphology quantification are still challenging. One of the morphological features of a cell, which has been shown to exhibit several genotypic and phenotypic properties of a cell is the orientation of the cell. Similarly, orientation of fibres has been shown to exhibit various properties of associated cell population, including their density, topography, etc.The orientation of cell and the associated fibres are the indicative of many physiological and disease conditions. While microscopy-based image acquisition is the traditional method to gain insights into the orientation and morphology of cell and associated fibres, extracting thequantitative information about the c...

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“…While phenomenological formulations are used to consider individual fibre families to be perfectly aligned with pre-defined directions, recent investigations additionally account for the dispersion of fibres; see Gasser et al (2006) for a formulation based on generalized structural tensors and Marquez (2006) in view of experimental investigations to identify the orientation of fibre distributions. In this contribution, we will incorporate the anisotropic fibre contributions by means of a microsphere model.…”

Section: Introductionmentioning

confidence: 99%

A microsphere-based remodelling formulation for anisotropic biological tissues

Menzel

Waffenschmidt

2009

Phil. Trans. R. Soc. A.

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Biological tissues possess the ability to adapt according to the respective local loading conditions, which results in growth and remodelling phenomena. The main goal of this work is the development of a new remodelling approach that, on the one hand, reflects the alignment of fibrous soft biological tissue with respect to representative loading directions. On the other hand, the continuum approach proposed is based on a sound micro-mechanically motivated formulation. To be specific, use of a worm-like chain model is made to describe the behaviour of long-chain molecules as present in, for instance, collageneous tissues. The extension of such a one-dimensional constitutive equation to the three-dimensional macroscopic level is performed by means of a microsphere formulation. Inherent with the algorithmic treatment of this type of modelling approach, a finite number of unit vectors is considered for the numerical integration over the domain of the unit sphere. As a key aspect of this contribution, remodelling is incorporated by setting up evolution equations for the referential orientations of these integration directions. Accordingly, the unit vectors considered now allow interpretation as internal variables, which characterize the material's anisotropic properties. Several numerical studies underline the applicability of the model that, moreover, nicely fits into iterative finite element formulations so that general boundary value problems can be solved.

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Fourier analysis and automated measurement of cell and fiber angular orientation distributions

Cited by 64 publications

References 18 publications

Comparison of 2D fiber network orientation measurement methods

Comparison of 2D fiber network orientation measurement methods

Extracting Cell and Fibre Orientation from Microscopic Images:Computational Methods and Tools

A microsphere-based remodelling formulation for anisotropic biological tissues

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