Document Detail

Sample sufficiency and number of modes to retain in statistical shape modelling.
MedLine Citation:
PMID:  18979775     Owner:  NLM     Status:  MEDLINE    
Statistical shape modelling is a popular technique in medical imaging, but the issue of sample size sufficiency is not generally considered. Also the number of principal modes retained is often chosen simply to cover a percentage of the total variance. We show that these simple rules are unreliable. We propose a new method that uses bootstrap replication and a t-test comparison with noise to decide whether each mode direction has stabilised. We establish mode correspondence by minimising the distance between the space spanned by the replicates and their mean. By retaining only stable modes, our method distinguishes real anatomical variation from modes dominated by random noise. This provides a lower stopping rule when the sample is small and converges as the sample size increases. We use this convergence to determine sample sufficiency. For validation we use synthetic datasets of the left ventricle generated with a known number of structural modes and added noise. Our stopping rule detected the correct number of modes to retain where other methods failed. The methods were also tested on real 2D (22 points) and 3D (500 points) face data, retaining 24 and 70 modes with sample sufficiency being reached at approximately 50 and 150 samples respectively. For a 3D database of the left ventricle (527 points), 319 samples are not sufficient, but at this level we can retain around 55 stable modes. Our method provides a principled foundation for appropriate selection of the number of modes to retain and determination of sample size sufficiency for statistical shape modelling.
Lin Mei; Michael Figl; Daniel Rueckert; Ara Darzi; Philip Edwards
Related Documents :
20885855 - Scaling properties of cell and organelle size.
3568695 - A sample size program for "proving the null hypothesis".
21964245 - Palatogenesis: engineering, pathways and pathologies.
22538825 - Sorption of lead, copper, and cadmium by calcium alginate. metal binding stoichiometry ...
23222415 - Comparison and clinical suitability of eight prediction models for cardiac surgery-rela...
16815055 - Spectral reconstruction methods in fast nmr: reduced dimensionality, random sampling an...
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention     Volume:  11     ISSN:  -     ISO Abbreviation:  Med Image Comput Comput Assist Interv     Publication Date:  2008  
Date Detail:
Created Date:  2008-11-04     Completed Date:  2008-12-09     Revised Date:  2009-12-11    
Medline Journal Info:
Nlm Unique ID:  101249582     Medline TA:  Med Image Comput Comput Assist Interv     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  425-33     Citation Subset:  IM    
Dept. of Biosurgery and Surgical Technology Imperial College London, UK.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Artificial Intelligence*
Computer Simulation
Face / anatomy & histology*
Heart Ventricles / radiography*
Image Enhancement / methods*
Image Interpretation, Computer-Assisted / methods*
Models, Biological
Models, Statistical
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Previous Document:  Hierarchical shape statistical model for segmentation of lung fields in chest radiographs.
Next Document:  Optimal feature point selection and automatic initialization in active shape model search.