Segmentation of dynamic PET or fMRI images based on a similarity metric
| Autor: | Miles N. Wernick, Yongyi Yang, Nikolas P. Galatsanos, Jovan G. Brankov |
|---|---|
| Rok vydání: | 2003 |
| Předmět: |
Nuclear and High Energy Physics
Fuzzy clustering Computer science business.industry Correlation clustering Pattern recognition Image segmentation Spectral clustering Euclidean distance Nuclear Energy and Engineering Metric (mathematics) Artificial intelligence Electrical and Electronic Engineering business Cluster analysis k-medians clustering |
| Zdroj: | IEEE Transactions on Nuclear Science. 50:1410-1414 |
| ISSN: | 1558-1578 0018-9499 |
| DOI: | 10.1109/tns.2003.817963 |
| Popis: | In this paper, we present a new approach for segmentation of image sequences by clustering the pixels according to their temporal behavior. The clustering metric we use is the normalized cross-correlation, also known as similarity. The main advantage of this metric is that, unlike the traditional Euclidean distance, it depends on the shape of the time signal rather than its amplitude. We model the intra-class variation among the time signals by a truncated exponential probability density distribution, and apply the expectation-maximization (EM) framework to derive two iterative clustering algorithms. Our numerical experiments using a simulated, dynamic PET brain study demonstrate that the proposed method achieves the best results when compared with several existing clustering methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|