Processing of Vertebral CT-Images for Diagnosis of Osteoporosis<br><br>

P. Saparin<br>
Institut f&uuml;r Physik, Universit&auml;t Potsdam<br><br>

Since in any object mass and structure depend on each other to build a
functional form, methodologies to measure structural information are
needed to understand the contribution of structure to support the form,
the complexity of the spatial architecture, the changes and the loss of
object integrity.  In the past, the structure of hierarchical natural
composites, especially biomaterials, has been evaluated merely by
parameters measuring parts of architectural elements such as thickness,
number of elements, the space in between, the volume, or the surface of
these elements. They are all based on invasive techniques.<br><br>

We propose a non-invasive technique to assess structure in its complex
spatial distribution by describing and quantifying the structural
architecture as a whole.  Vertebral computed tomography (CT) images were
preprocessed by newly developed algorithms which separate the vertebra
from the soft tissue and then split the entire vertebra into the
trabecular bone and cortical shell.  Next the concept of measures of
complexity based on symbolic dynamics is applied to segmented CT-images
which are, crucially important, symbol-encoded by both static and
dynamical approaches. To study their structural properties, we
generalize the concept of symbolic dynamics into two-dimensional case.<br><br>

This method is employed to identify structural changes in human
cancellous bone of vertebral bodies and leads to new insides for the
understanding of bone's internal structure.  The results give the first
experimental and quantitative evidence that the complexity of cancellous
bone structure declines rapidly with increased disintegration of bone
and exponentially relates to the amount of material it is built from.
In addition, this method is significantly sensitive to changes in
structure and provides improvements of differentiation of structural
loss and may well have an impact beyond biological and physical science,
expending materials science such as metallurgy, bioengineering, and
mechanical engineering.