Automatic Detection of the Back Valley on Scoliotic Trunk Using Polygonal Surface Curvature

Authors:
J. Thériault;F. Cheriet;F. Guibault
Affiliations:
Department of Computer Engineering, École Polytechnique de Montréal, Canada;Department of Computer Engineering, École Polytechnique de Montréal, Canada;Department of Computer Engineering, École Polytechnique de Montréal, Canada
Venue:
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Year:
2008

Citing 10
Cited 0

Surface shape and curvature scales

Image and Vision Computing
Optimal triangulation and quadric-based surface simplification

Computational Geometry: Theory and Applications - Special issue on multi-resolution modelling and 3D geometry compression
On surface normal and Gaussian curvature approximations given data sampled from a smooth surface

Computer Aided Geometric Design
Estimating the tensor of curvature of a surface from a polyhedral approximation

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A novel cubic-order algorithm for approximating principal direction vectors

ACM Transactions on Graphics (TOG)
3D characterization and localization of anatomical landmarks of the foot by FastSCAN

Real-Time Imaging - Special issue on imaging in bioinformatics: Part III
An efficient and robust algorithm for 3D mesh segmentation

Multimedia Tools and Applications
Finding ridges and valleys in a discrete surface using a modified MLS approximation

Computer-Aided Design
Prediction of anterior scoliotic spinal curve from trunk surface using support vector regression

Engineering Applications of Artificial Intelligence
Feature detection using curvature maps and the min-cut/max-flow algorithm

GMP'06 Proceedings of the 4th international conference on Geometric Modeling and Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

The objective of this paper is to automatically detect the back valley on a polygonal mesh of the human trunk surface. A 3D camera system based on the projection of a structured light is used for the acquisition of the whole trunk of scoliotic patients. A quadratic fitting method is used to calculate the principal curvatures for each vertex. It was determined that 3 levels of neighbors were sufficient to detect the back valley. The proposed method was evaluated on a set of 61 surface trunks of scoliotic patients. The results were validated by two orthopedic surgeons and were estimated to 84% of success in the automatic detection of the back valley. The proposed method is reproducible and could be useful for clinical assessment of scoliosis severity and a non-invasive progression follow-up.