Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

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.

Original publication

DOI

10.1109/IEMBS.2010.5626787

Type

Journal article

Journal

Conf Proc IEEE Eng Med Biol Soc

Publication Date

2010

Volume

2010

Pages

5589 - 5592

Keywords

Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Tendons, Uncertainty