title: Hierarchical manifold learning.
abstract: We present a novel method of hierarchical manifold learning which aims to automatically discover regional variations within images. This involves constructing manifolds in a hierarchy of image patches of increasing granularity, while ensuring consistency between hierarchy levels. We demonstrate its utility in two very different settings: (1) to learn the regional correlations in motion within a sequence of time-resolved images of the thoracic cavity; (2) to find discriminative regions of 3D brain images in the classification of neurodegenerative disease,
authors.affiliation: Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK.
authors.forename: Kanwal K
authors.initials: KK
authors.lastname: Bhatia
authors.forename: Anil
authors.initials: A
authors.lastname: Rao
authors.forename: Anthony N
authors.initials: AN
authors.lastname: Price
authors.forename: Robin
authors.initials: R
authors.lastname: Wolz
authors.forename: Jo
authors.initials: J
authors.lastname: Hajnal
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis / Q000473:pathology; D001185:Artificial Intelligence; D001331:Automation; D001921:Brain / Q000473:pathology; D006321:Heart / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D015233:Models, Statistical; D019636:Neurodegenerative Diseases / Q000175:diagnosis* / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012984:Software; D035423:Thoracic Cavity / Q000473:pathology*; D013997:Time Factors