Short communicationTendon extracellular matrix damage detection and quantification using automated edge detection analysis
Introduction
Accumulation of fiber microtrauma with repetitive sub-threshold loading is a contributory factor to the pathogenesis of tendinopathy (Renstrom and Johnson, 1985). In addition to impairing mechanical function (Fung et al., 2009, Andarawis-Puri et al., 2011), tendon matrix damage also likely affects tenocyte homeostasis (Andarawis-Puri et al., 2012). Technical methods for quantifying the extent of local structural damage in biological injury models are critical for understanding the disease process.
Collagen fibril alignment, and thus matrix damage, has been measured using various techniques including FFT (Fung et al., 2010, Chaudhuri et al., 1987) and polarized light (Dickey et al., 1998, Thomopoulos et al., 2006). Fung et al. utilized second harmonic generation (SHG) microscopy to image type I collagen to study damage accumulation in a rat patellar tendon overuse model and found damage patterns progressed with fatigue injury from initial small fiber kink deformations, to fiber dissociations, to higher level fiber discontinuities and tendon rupture (Fung et al., 2010). We have previously developed a fast Fourier transform (FFT) method to quantify fiber alignment without bias and inter-rater variability and showed increasing levels of fiber deformation with progressive fatigue levels (Fung et al., 2010). Here we present a novel image processing technique based on edge detection, which has not been reported in the tendon or ligament literature that enables quantification of local fibril orientation and damage region segmentation. Edge detection has been previously applied in biological studies studying cellular and cytoskeletal alignment (Kemeny and Clyne, 2011, Karlon et al., 1999, Yoshigi et al., 2003, Vartanian et al., 2008), but has not been utilized to study tendon damage. In addition to identifying damage areas, the presented algorithm expands on our previous methods by classifying damage regions by area and severity. The method is computationally efficient and enables calculation of angular orientation at the fibril level.
Edge detection finds edges by calculating intensity changes and determining the orientation of the maximum intensity gradient (Karlon et al., 1999, Yoshigi et al., 2003, Kaunas et al., 2005). The Laplacian is found in two directions, x and y, and an intensity gradient vector is found for each pixel. The local orientation is normal to the direction of the intensity gradient vector. Sobel operators, which approximate the gradient of intensity in both horizontal (Eq. (1)) and vertical (Eq. (2)) directions have been used to reduce gradient computation times (Sobel and Feldman, 1968, Duda and Hart, 1973, Yoshigi et al., 2003). The matrix operators, Kx and Ky, are applied separately to intensity values at each pixel, Iij, and surrounding neighbors to produce measurements of the gradient component, Gx (Eq. (3)) and Gy (Eq. (4)), in which ⁎ denotes a 2-D convolution operation (Duda and Hart, 1973, Yoshigi et al., 2003). Magnitude (G) and direction (Θ) of the pixel intensity gradient is determined by Eqs. (5), (6) (Duda and Hart, 1973, Yoshigi et al., 2003), respectively. MATLAB's® element-by-element calculation and fast filtering techniques can implement edge detection algorithms that are robust to noise and computationally efficient (Kemeny and Clyne, 2011).
Section snippets
Specimen preparation
Rat tail tendons (RTTs) from nine month old, female Sprague-Dawley rats (n=5) were harvested and placed in phosphate-buffered saline (PBS). RTTs were gripped between custom sand-paper grips for a 50 mm length and mounted in a materials testing system (Instron 8872, MA) with a 2.5 lb load cell (Transducer Techniques, CA) and 37 °C PBS bath. Tendons were divided into groups, one group of non-loaded controls and remaining groups fatigue loaded to 2.5% strain at 0.5 Hz, sinusoidal waveform for 50, 100,
Assessment of angular orientations in damage severity groups
Since artificial angles were used to bin segmentations by severity, estimations of true angular deviations within these regions were investigated. High damage regions had true mean angular deviations of 30° from normal and top 10% of angles greater than 38° from normal. Moderate damage regions had true mean angular deviations between 25° and 30° from normal and top 10% of angles greater than 32° from normal. Low damage regions had true mean angular deviations less than 25° from normal and top
Discussion
The presented method provides the unique ability to identify, quantify, and stratify damage by severity and represents progress in our ability to accurately quantify and assess fatigue damage in overuse models. Qualitative inspection of damage regions identified by the algorithm found that the routine correctly identified and closely outlined damage for the full range of damage expected. Damage criteria developed for automated image analysis corresponded to qualitative manual assessments,
Conclusions
The presented technique is the first implementation of edge detection to analyze collagen orientation in fatigued tendon and the first program developed to segment and quantify matrix damage by area and severity. Correlation between automated and manual damage segmentations showed the automated technique is acceptable, enabling non-biased and reproducible analysis of tendon images that significantly reduces analysis time. The algorithm represents an important advancement in tendon damage
Conflict of interest statement
All authors on this manuscript (Stephen J. Ros, Ph.D., Nelly Andarawis-Puri, Ph.D., and Evan L. Flatow, M.D.) have no financial or personal relationships with other people or organizations that could inappropriately influence or bias this work.
Acknowledgments
The authors would like to acknowledge the following for their contributions to this study: Victor Friedrich, Ph.D., Rumana Huq, and Nisha George.
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