Quantitative analysis of tendon ECM damage using MRI

Szilágyi, Tünde; Harvey, Adam; Brady, Sir Michael; Cochlin, Lowri E; Joshi, Niranjan

doi:10.1109/isbi.2010.5490251

Cited by 1 publication

(3 citation statements)

References 7 publications

(10 reference statements)

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“…South pole of the sphere. Ci1D values were estimated using the framework proposed in [4], with orientation derived from the conformal monogenic signal and noise model estimated from the above iterative process using NP windows. We used NP windows to avoid bias of the noise model, to get smooth estimates of the noise pdfs even with few samples, and to take in account spatial dependence between neighbouring pixels in a realistic computation time.…”

Section: Image Descriptors and 1d Energy Mapmentioning

confidence: 99%

“…The 1D image energy map was derived using the phase congruence feature model weighted with the ci1D values as local spread [4]. This gives our uncertainty of the derived curvature values.…”

Section: Image Descriptors and 1d Energy Mapmentioning

confidence: 99%

“…Assuming regular fascicular banding (i.e. linearity) for normal tendon, we have shown that damaged regions of tendon can be identified from high field MR images, using a the bi-modality of intrinsically 1D structures [4]. Structurally important at the microscopic level is a visual feature of longitudinally sectioned tendon known as crimp [1], [2].…”

Section: Introductionmentioning

confidence: 96%

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Curvatures with uncertainties derived in conformal space to characterize tendon microstructure

Harvey

Szilágyi

Brady

et al. 2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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There is a growing demand for non-invasive methods to diagnose tendon injuries and monitor the healing processes of their repair. To date there is limited knowledge on their structure and function and the interlink between these. One of the potential targets in this investigation is the extracellular matrix (ECM) that captures its structural changes. Recently we reported on the assessment tendon damage on a macroscopic level from high field MR signals. In this paper, we present a methodology that enables structural description on a microscopic level. We derived curvature values from the conformal monogenic signal, which however can become unreliable in the presence of noise. To account for this we use non parametric noise properties and a 1D feature based uncertainty measure in an iterative framework using Hidden Markov Measure Field (HMMF). The proposed method reveals that curvature values derived from normal tendon tissue microscopy images are higher and more homogenous than curvature values derived from the damaged tendon images.

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Section: Image Descriptors and 1d Energy Mapmentioning

confidence: 99%

“…The 1D image energy map was derived using the phase congruence feature model weighted with the ci1D values as local spread [4]. This gives our uncertainty of the derived curvature values.…”

Section: Image Descriptors and 1d Energy Mapmentioning

confidence: 99%