In this paper we present a new approach for clustering of time-sequence imaging data. The clustering metric used is the normalized cross-correlation, also known as similarity. The main advantage of this metric over the more-traditional Euclidean distance, is that it depends on the signal's shape rather than its amplitude. Under an assumption of an exponential probability model that has several desirable properties, the expectation-maximization (EM) framework is used to derive two iterative clustering algorithms. In numerical experiments based on a simulated dynamic PET brain study, the proposed method achieved better performance than several existing clustering methods.
CITATION STYLE
Brankov, J. G., Galatsanos, N. P., Yang, Y., & Wernick, M. N. (2002). Image Sequence Segmentation Based on a Similarity Metric. In IEEE Nuclear Science Symposium and Medical Imaging Conference (Vol. 2, pp. 1211–1216). https://doi.org/10.1109/nssmic.2002.1239538
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