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.
authors: University of California, Davis, CA 95616, USA. dfalcantara@ucdavis.edu
authors: Dan A
authors: DA
authors: Alcantara
authors: Owen
authors: O
authors: Carmichael
authors: Will
authors: W
authors: Harcourt-Smith
authors: Kirstin
authors: K
authors: Sterner
authors: Stephen R
authors: SR
authors: Frost
authors: Rebecca
authors: R
authors: Dutton
authors: Paul
authors: P
authors: Thompson
authors: Eric
authors: E
authors: Delson
authors: Nina
authors: N
authors: Amenta
mesh_terms: 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