Semi-supervised cluster analysis of imaging data

• Published in: NeuroImage (Orlando, Fla.) Vol. 54; no. 3; pp. 2185 - 2197
• Main Authors: Filipovych, Roman, Resnick, Susan M., Davatzikos, Christos
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2011; Elsevier Limited
• Subjects: Aged; Aged, 80 and over; Aging; Algorithms; Artificial Intelligence; Atrophy; Brain - pathology; Classification; Cluster Analysis; Cognition - physiology; Cognitive ability; Computer Simulation; Disease; Humans; Image Processing, Computer-Assisted - statistics & numerical data; Longitudinal Studies; Magnetic Resonance Imaging - methods; MCI; Middle Aged; Models, Neurological; MRI; Neighborhoods; Neuropsychological Tests; Older people; Pathology; Patients; Principal Component Analysis; Semi-supervised pattern analysis; Studies; Cluster analysis; Aging; Semi-supervised pattern analysis; MCI; MRI
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2010.09.074

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. ►Clustering allows to understand the structure of the populations of older adults. ►A cluster-based measure of pathology correlates well with clinical data. ►Cognitively stable older adults tend to form more homogeneous subpopulations.