Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
An Approximate Distribution for the Normalized Cut
Journal of Mathematical Imaging and Vision
Task-Specific Functional Brain Geometry from Model Maps
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Artificial Intelligence in Medicine
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The blood oxygen level-dependent (BOLD) signal in response to brief periods of stimulus can be detected using event-related functional magnetic resonance imaging (ER-fMRI). In this paper, we propose a new approach for the analysis of ER-fMRI data. We regard the time series as vectors in a high dimensional space (the dimension is the number of time samples). We believe that all activated times series share a common structure and all belong to a low dimensional manifold. On the other hand, we expect the background time series (after detrending) to form a cloud around the origin. We construct an embedding that reveals the organization of the data into an activated manifold and a cluster of non-activated time series. We use a graph partitioning technique–the normalized cut to find the separation between the activated manifold and the background time series. We have conducted several experiments with synthetic and in-vivo data that demonstrate the performance of our approach.