Exploration of shape variation using localized components analysis

Dan A. Alcantara; Owen Carmichael; Will Harcourt-Smith; Kirstin Sterner; Stephen R. Frost; Rebecca Dutton; Paul Thompson; Eric Delson; Nina Amenta

doi:10.1109/TPAMI.2008.287

Exploration of shape variation using localized components analysis

Dan A. Alcantara, Owen Carmichael, Will Harcourt-Smith, Kirstin Sterner, Stephen R. Frost, Rebecca Dutton, Paul Thompson, Eric Delson, Nina Amenta

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

Localized Components Analysis (LoCA) is a new method for describing surface shape variation in an ensemble of objects using a linear subspace of spatially localized shape components. In contrast to earlier methods, LoCA optimizes explicitly for localized components and allows a flexible trade-off between localized and concise representations, and the formulation of locality is flexible enough to incorporate properties such as symmetry. This paper demonstrates that LoCA can provide intuitive presentations of shape differences associated with sex, disease state, and species in a broad range of biomedical specimens, including human brain regions and monkey crania.

Original language	English
Pages (from-to)	1510-1516
Number of pages	7
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	31
Issue number	8
DOIs	https://doi.org/10.1109/TPAMI.2008.287
State	Published - 2009
Externally published	Yes

Keywords

Feature representation
Life and medical sciences
Size and shape

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10.1109/TPAMI.2008.287

Cite this

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title = "Exploration of shape variation using localized components analysis",

abstract = "Localized Components Analysis (LoCA) is a new method for describing surface shape variation in an ensemble of objects using a linear subspace of spatially localized shape components. In contrast to earlier methods, LoCA optimizes explicitly for localized components and allows a flexible trade-off between localized and concise representations, and the formulation of locality is flexible enough to incorporate properties such as symmetry. This paper demonstrates that LoCA can provide intuitive presentations of shape differences associated with sex, disease state, and species in a broad range of biomedical specimens, including human brain regions and monkey crania.",

keywords = "Feature representation, Life and medical sciences, Size and shape",

author = "Alcantara, {Dan A.} and Owen Carmichael and Will Harcourt-Smith and Kirstin Sterner and Frost, {Stephen R.} and Rebecca Dutton and Paul Thompson and Eric Delson and Nina Amenta",

note = "Funding Information: This research was funded by grants US National Science Foundation (NSF) IIS-0513660 and NSF IIS-0513894. The authors thank Professor Howard Aizenstein, Professor Oscar Lopez, and Professor James Becker for their roles in collecting the CC and ventricle data. This is NYCEP Morphometrics contribution number 32.",

year = "2009",

doi = "10.1109/TPAMI.2008.287",

language = "English",

volume = "31",

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journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",

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T1 - Exploration of shape variation using localized components analysis

AU - Alcantara, Dan A.

AU - Carmichael, Owen

AU - Harcourt-Smith, Will

AU - Sterner, Kirstin

AU - Frost, Stephen R.

AU - Dutton, Rebecca

AU - Thompson, Paul

AU - Delson, Eric

AU - Amenta, Nina

N1 - Funding Information: This research was funded by grants US National Science Foundation (NSF) IIS-0513660 and NSF IIS-0513894. The authors thank Professor Howard Aizenstein, Professor Oscar Lopez, and Professor James Becker for their roles in collecting the CC and ventricle data. This is NYCEP Morphometrics contribution number 32.

PY - 2009

Y1 - 2009

N2 - Localized Components Analysis (LoCA) is a new method for describing surface shape variation in an ensemble of objects using a linear subspace of spatially localized shape components. In contrast to earlier methods, LoCA optimizes explicitly for localized components and allows a flexible trade-off between localized and concise representations, and the formulation of locality is flexible enough to incorporate properties such as symmetry. This paper demonstrates that LoCA can provide intuitive presentations of shape differences associated with sex, disease state, and species in a broad range of biomedical specimens, including human brain regions and monkey crania.

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KW - Size and shape

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Exploration of shape variation using localized components analysis

Abstract

Keywords

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