Exploration of shape variation using localized components analysis.
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.
Alcantara
Dan A
DA
University of California, Davis, CA 95616, USA. dfalcantara@ucdavis.edu
Carmichael
Owen
O
Harcourt-Smith
Will
W
Sterner
Kirstin
K
Frost
Stephen R
SR
Dutton
Rebecca
R
Thompson
Paul
P
Delson
Eric
E
Amenta
Nina
N
D000328:Adult; D000704:Analysis of Variance; D000818:Animals; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology; D001931:Brain Mapping; D002520:Cercopithecidae; D003337:Corpus Callosum / Q000033:anatomy & histology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D020547:Lateral Ventricles / Q000033:anatomy & histology; D019028:Magnetic Resonance Imaging, Cine; D008297:Male; D008875:Middle Aged; D025341:Principal Component Analysis / Q000379:methods*; D012886:Skull / Q000033:anatomy & histology