Hierarchical manifold learning

Authors:
Kanwal K. Bhatia;Anil Rao;Anthony N. Price;Robin Wolz;Jo Hajnal;Daniel Rueckert
Affiliations:
Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK;Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK;Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK;Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK;Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK;Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2012

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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.