NDLI: Statistical shape modelling: How many modes should be retained?

Content Provider	IEEE Xplore Digital Library
Author	Lin Mei Figl, M. Rueckert, D. Darzi, A. Edwards, P.
Copyright Year	2008
Description	Author affiliation: Dept. of Biosurgery & Surg. Technol., Imperial Coll. London, London (Lin Mei; Figl, M.; Rueckert, D.; Darzi, A.; Edwards, P.)
Abstract	Statistical shape modelling is a technique whereby the variation of shape across the population is modelled by principal component analysis (PCA) on a set of sample shape vectors. The number of principal modes retained in the model (PCA dimension) is often determined by simple rules, for example choosing those cover a percentage of total variance. We show that this rule is highly dependent on sample size. The principal modes retained should ideally correspond to genuine anatomical variation. In this paper, we propose a mathematical framework for analysing the source of PCA model error. The optimum PCA dimension is a pay-off between discarding structural variation (under-modelling) and including noise (over-modelling). We then propose a stopping rule that identifies the noise dominated modes using a t-test of the bootstrap stability between the real data and artificial Gaussian noise. We retain those modes that are not dominated by noise. We show that our method determines the correct PCA dimension for synthetic data, where conventional rules fail. Comparison between our rule and conventional rules are also performed on a series of real datasets. We provide a foundation for analysing rules that are used to determine the number of modes to retain and also allows the study of PCA sample sufficiency.
Starting Page	1
Ending Page	8
File Size	1518537
Page Count	8
File Format	PDF
ISBN	9781424423392
ISSN	21607508
DOI	10.1109/CVPRW.2008.4562996
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image analysis Hospitals Stability Gaussian noise Surgery Educational institutions Active shape model Mathematical model Data mining Principal component analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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