title: Kernel-based atlas image selection for brain tissue segmentation.
abstract: We propose a new Kernel-based Atlas Image Selection computed in the Embedding Representation space (termed KAISER) aiming to support labeling of brain tissue on 3D magnetic resonance (MR) images. KAISER approach provides efficient feature extraction from MR volumes based on an introduced inter-slice kernel (ISK). Thus, using the ISK matrix eigendecomposition, the inherent structure of data distribution is accentuated through estimation of low dimensional compact space where every pair-wise image similarity can be better measured. We compare our proposal against the whole-population atlas, randomly and demographically selected multiatlas approaches in a four-tissue image labeling task. Obtained results show that the KAISER approach outperforms other alternative techniques (98% Dice index similarity against 94%), while exhibiting better repeatability.
authors.forename: D
authors.initials: D
authors.lastname: Cárdenas-Peña
authors.forename: M
authors.initials: M
authors.lastname: Orbes-Arteaga
authors.forename: G
authors.initials: G
authors.lastname: Castellanos-Dominguez
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D001921:Brain / Q000473:pathology*; D006801:Humans; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000379:methods; D008875:Middle Aged; D015203:Reproducibility of Results; D012984:Software; D055815:Young Adult