Semi-supervised cluster analysis of imaging data

Roman Filipovych, Susan M. Resnick, Christos Davatzikos

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


In this paper, we present a semi-supervised clustering-based framework for discovering coherent subpopulations in heterogeneous image sets. Our approach involves limited supervision in the form of labeled instances from two distributions that reflect a rough guess about subspace of features that are relevant for cluster analysis. By assuming that images are defined in a common space via registration to a common template, we propose a segmentation-based method for detecting locations that signify local regional differences in the two labeled sets. A PCA model of local image appearance is then estimated at each location of interest, and ranked with respect to its relevance for clustering. We develop an incremental k-means-like algorithm that discovers novel meaningful categories in a test image set. The application of our approach in this paper is in analysis of populations of healthy older adults. We validate our approach on a synthetic dataset, as well as on a dataset of brain images of older adults. We assess our method's performance on the problem of discovering clusters of MR images of human brain, and present a cluster-based measure of pathology that reflects the deviation of a subject's MR image from normal (i.e. cognitively stable) state. We analyze the clusters' structure, and show that clustering results obtained using our approach correlate well with clinical data.

Original languageEnglish (US)
Pages (from-to)2185-2197
Number of pages13
Issue number3
StatePublished - Feb 1 2011
Externally publishedYes


  • Aging
  • Cluster analysis
  • MCI
  • MRI
  • Semi-supervised pattern analysis

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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