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Erschienen in:
A Volumetric Conformal Mapping Approach for Clustering White Matter Fibers in the Brain Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Longitudinal Scoliotic Trunk Analysis via Spectral Representation and Statistical Analysis

verfasst von : Ola Ahmad, Herve Lombaert, Stefan Parent, Hubert Labelle, Jean Dansereau, Farida Cheriet

Erschienen in: Spectral and Shape Analysis in Medical Imaging

Verlag: Springer International Publishing

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Abstract

Scoliosis is a complex 3D deformation of the spine leading to asymmetry of the external shape of the human trunk. A clinical follow-up of this deformation is decisive for its treatment, which depends on the spinal curvature but also on the deformity’s progression over time. This paper presents a new method for longitudinal analysis of scoliotic trunks based on spectral representation of shapes combined with statistical analysis. The spectrum of the surface model is used to compute the correspondence between deformable scoliotic trunks. Spectral correspondence is combined with Canonical Correlation Analysis to do point-wise feature comparison between models. This novel combination allows us to efficiently capture within-subject shape changes to assess scoliosis progression (SP). We tested our method on 23 scoliotic patients with right thoracic curvature. Quantitative comparison with spinal measurements confirms that our method is able to identify significant changes associated with SP.

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Vorheriges Kapitel Spatio-Temporal Shape Analysis of Cross-Sectional Data for Detection of Early Changes in Neurodegenerative Disease
Nächstes Kapitel Statistical Shape Model with Random Walks for Inner Ear Segmentation
Fußnoten
1
The Radial Basis Functions (RBF) algorithm [6] is used to interpolate incomplete trunk meshes and to enforce mesh connectivity.
 
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Metadaten
Titel
Longitudinal Scoliotic Trunk Analysis via Spectral Representation and Statistical Analysis
verfasst von
Ola Ahmad
Herve Lombaert
Stefan Parent
Hubert Labelle
Jean Dansereau
Farida Cheriet
Copyright-Jahr
2016
Buch
Spectral and Shape Analysis in Medical Imaging

Print ISBN: 978-3-319-51236-5

Electronic ISBN: 978-3-319-51237-2

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-51237-2_7