Statistical Shape Space Analysis Based on Level Sets

Authors:
Nina Kozic;Miguel Á. González Ballester;Moritz Tannast;Lutz P. Nolte;Mauricio Reyes
Affiliations:
MEM Research Center, Institute for Surgical Technology and Biomechanics, University of Bern, Switzerland;MEM Research Center, Institute for Surgical Technology and Biomechanics, University of Bern, Switzerland;Department of Orthopaedic Surgery, Inselspital University of Bern, Switzerland;MEM Research Center, Institute for Surgical Technology and Biomechanics, University of Bern, Switzerland;MEM Research Center, Institute for Surgical Technology and Biomechanics, University of Bern, Switzerland
Venue:
MIAR '08 Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality
Year:
2008

Citing 9
Cited 0

Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations

Journal of Computational Physics
A fast level set method for propagating interfaces

Journal of Computational Physics
Active shape models—their training and application

Computer Vision and Image Understanding
Parametrization of closed surfaces for 3-D shape description

Computer Vision and Image Understanding
A variational level set approach to multiphase motion

Journal of Computational Physics
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Level Set Based Segmentation with Intensity and Curvature Priors

MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Active contours without edges

IEEE Transactions on Image Processing
Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A framework for optimisation of specific criteria across the shape variability found in a population is proposed. The method is based on level set segmentation in the parametric space defined by Principal Component Analysis (PCA). The efficient narrow band evolution of the level set allows to search for the instances only in the neighborhood of the zero level set and not in the whole shape space. We are able to optimise any given criterion not to provide a single best fitting instance in the shape space, but rather to provide a group of instances that meet the criterion. This effectively defines a partition in the shape space, which can have any topology. The method works for data of any dimension, determined by the number of principal components retained. Results are shown on the application to shape analysis of human femora.