Abstract
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 language | English (US) |
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Pages (from-to) | 2185-2197 |
Number of pages | 13 |
Journal | NeuroImage |
Volume | 54 |
Issue number | 3 |
DOIs | |
State | Published - Feb 1 2011 |
Externally published | Yes |
Keywords
- Aging
- Cluster analysis
- MCI
- MRI
- Semi-supervised pattern analysis
ASJC Scopus subject areas
- Neurology
- Cognitive Neuroscience