http://www.cnr.it/ontology/cnr/individuo/prodotto/ID285095
Radon transform: Image reconstruction and identification of noise and instrumental artifacts (Contributo in atti di convegno)
- Type
- Label
- Radon transform: Image reconstruction and identification of noise and instrumental artifacts (Contributo in atti di convegno) (literal)
- Anno
- 2014-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1109/SIU.2014.6830720 (literal)
- Alternative label
D'Acunto M.; Benassi A.; Moroni D.; Salvetti O. (2014)
Radon transform: Image reconstruction and identification of noise and instrumental artifacts
in 22nd Signal Processing and Communications Applications Conference (SIU 2014), Trabzon, Turkey, 23-25 April 2014
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- D'Acunto M.; Benassi A.; Moroni D.; Salvetti O. (literal)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://www.scopus.com/inward/record.url?eid=2-s2.0-84903774703&partnerID=q2rCbXpz (literal)
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- Scopu (literal)
- PuMa (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Istituto di Struttura della Materia, Istituto di Scienza e Tecnologie dell'Informazione, Consiglio Nazionale Delle Ricerche, via Moruzzi 1, 56124, Pisa, Italy; Istituto di Scienza e Tecnologie dell'Informazione, Consiglio Nazionale Delle Ricerche, via Moruzzi 1, 56124, Pisa, Italy (literal)
- Titolo
- Radon transform: Image reconstruction and identification of noise and instrumental artifacts (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
- 978-1-4799-4874-1 (literal)
- Abstract
- Computed Tomography as well as Magnetic Resonance or Positron Electron Tomography are currently the most commonly used medical imaging modalities for the analysis of human body complex structures and organs, where diseases must be recognized and identified. The image reconstruction process used in these tomography techniques is usually based on the Radon Transform (RT). In this paper, an algorithm including correction of noise and of some instrumental artifacts directly from the RT sinograms is presented. The innovative contribution of our algorithm is based on the fact that it is not necessary a priori information on instrumental artifacts or noise sources. In addition, the algorithm can be applied to any RT-based medical imaging technologies. © 2014 IEEE. (literal)
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