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Multi-scale Analysis of Imaging Features and Its Use in the Study of COPD Exacerbation Susceptible Phenotypes

  • Felix J. S. Bragman
  • Jamie R. McClelland
  • Marc Modat
  • Sébastien Ourselin
  • John R. Hurst
  • David J. Hawkes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

We propose a novel framework for exploring patterns of respiratory pathophysiology from paired breath-hold CT scans. This is designed to enable analysis of large datasets with the view of determining relationships between functional measures, disease state and the likelihood of disease progression. The framework is based on the local distribution of image features at various anatomical scales. Principal Component Analysis is used to visualise and quantify the multi-scale anatomical variation of features, whilst the distribution subspace can be exploited within a classification setting. This framework enables hypothesis testing related to the different phenotypes implicated in Chronic Obstructive Pulmonary Disease (COPD). We illustrate the potential of our method on initial results from a subset of patients from the COPDGene study, who are exacerbation susceptible and non-susceptible.

Keywords

Chronic Obstructive Pulmonary Disease Principal Component Score Lagrangian Strain Tensor Gaussian Radial Basis Function Kernel Chronic Obstructive Pulmonary Disease Phenotype 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Felix J. S. Bragman
    • 1
  • Jamie R. McClelland
    • 1
  • Marc Modat
    • 1
  • Sébastien Ourselin
    • 1
  • John R. Hurst
    • 2
  • David J. Hawkes
    • 1
  1. 1.Centre for Medical Image ComputingUniversity College LondonUK
  2. 2.Centre for Inflammation and Tissue RepairUniversity College LondonUK

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