Artificial enlargement of a training set for statistical shape models: application to cardiac images

Authors:
J. Lötjönen;K. Antila;E. Lamminmäki;J. Koikkalainen;M. Lilja;T. Cootes
Affiliations:
VTT Information Technology, Tampere, Finland;VTT Information Technology, Tampere, Finland;VTT Information Technology, Tampere, Finland;Laboratory of Biomedical Engineering, Helsinki University of Technology, HUT, Finland;Laboratory of Biomedical Engineering, Helsinki University of Technology, HUT, Finland;Division of Imaging Science and Biomedical Engineering, University of Manchester, U.K
Venue:
FIMH'05 Proceedings of the Third international conference on Functional Imaging and Modeling of the Heart
Year:
2005

Citing 4
Cited 3

Active shape models—their training and application

Computer Vision and Image Understanding
An Adaptive-Focus Deformable Model Using Statistical and Geometric Information

IEEE Transactions on Pattern Analysis and Machine Intelligence
Individualizing Neuroanatomical Atlases Using a Massively Parallel Computer

Computer
Elastic Matching Using a Deformation Sphere

MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention

Localized maximum entropy shape modelling

IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Subject specific shape modeling with incremental mixture models

MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
A locally deformable statistical shape model

MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging

Quantified Score

Hi-index	0.00

Visualization

Abstract

Different methods were evaluated to enlarge artificially a training set which is used to build a statistical shape model. In this work, the shape model was built from MR data of 25 subjects and it consisted of ventricles, atria and epicardium. The method adding smooth non-rigid deformations to original training set examples produced the best results. The results indicated also that artificial deformation modes model better an unseen object than an equal number of standard PCA modes generated from original data.