Statistical Shape Analysis for Computer Aided Spine Deformation Detection


In this paper we describe a medical application where we exploit
surface properties (measured in form of 3D-Range scans of the
human back) to derive a-priori unknown additional properties of
the proband, that otherwise can only be acquired using multiple
x-ray recordings or volumetric scans as CT or MRI. On the basis of
274 data sets, we perform classification using statistical shape
analysis methods. Consistent parameterization and alignment is
achieved on the basis of only few anatomic landmarks. As our
choice of landmarks is easy to detect on the human body, our
approach is feasible for screening applications that can be
expected to have much impact on the early detection and later
treatment of spine deformities, in particular scoliosis.