Statistical Shape Analysis for Computer Aided Spine Deformity Detection

V. Skala (Editoren)
Gerhard H. Bendels, Reinhard Klein, M. Samimi und A. Schmitz
In: Journal of WSCG (Feb. 2005), 13:2(57-64)
Präsentiert: The 13-th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2005 (WSCG'2005)
 

Abstract

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.

Stichwörter: medical assistance, PCA, scoliosis, statistical shape analysis

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Bibtex

@ARTICLE{bendels-2005-statistical,
      author = {Bendels, Gerhard H. and Klein, Reinhard and Samimi, M. and Schmitz, A.},
      editor = {Skala, V.},
       pages = {57--64},
       title = {Statistical Shape Analysis for Computer Aided Spine Deformity Detection},
     journal = {Journal of WSCG},
      volume = {13},
      number = {2},
        year = {2005},
       month = feb,
   publisher = {UNION Agency-Science Press},
    keywords = {medical assistance, PCA, scoliosis, statistical shape analysis},
    abstract = {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.},
        issn = {1213-6972},
  conference = {The 13-th International Conference in Central Europe on Computer Graphics, Visualization and
                Computer Vision 2005 (WSCG'2005)}
}