Entropic Estimation of Noise for Medical Volume Restoration
| Autor: | E. Keeve, Marc Lievin, Franck Luthon |
|---|---|
| Přispěvatelé: | Laboratoire des images et des signaux (LIS), Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2002 |
| Předmět: |
Markov chain
business.industry Wiener filter Markov process Pattern recognition 02 engineering and technology 030218 nuclear medicine & medical imaging Adaptive filter 03 medical and health sciences symbols.namesake 0302 clinical medicine [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing Gaussian noise 0202 electrical engineering electronic engineering information engineering symbols Entropy (information theory) 020201 artificial intelligence & image processing [INFO]Computer Science [cs] Artificial intelligence Cluster analysis business Image restoration Mathematics |
| Zdroj: | 16th International Conference on Pattern Recognition, ICPR 2002 16th International Conference on Pattern Recognition, ICPR 2002, Aug 2002, Quebec, Canada. pp.871-874, ⟨10.1109/ICPR.2002.1048166⟩ Scopus-Elsevier ICPR (3) |
| DOI: | 10.1109/ICPR.2002.1048166⟩ |
| Popis: | International audience; This paper presents an unsupervised approach for medical volume restoration. To cope with various scanning modalities and strongly corrupted data, an original information tool is introduced: the entropic deviation. To validate the robustness of this estimation, a non-linear restoration filter based on Markov random fields is proposed. No parameter tuning is required from the user thanks to the adaptive value of the entropy power Finally, the good quality of the filtered volumes are promising for any clustering application aiming at anatomical structures extraction in medical volume datasets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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