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Titolo:
Contextual clustering for analysis of functional MRI data
Autore:
Salli, E; Aronen, HJ; Savolainen, S; Korvenoja, A; Visa, A;
Indirizzi:
Helsinki Univ Technol, Biomed Engn Lab, FIN-02015 Espoo, Finland Helsinki Univ Technol Espoo Finland FIN-02015 , FIN-02015 Espoo, Finland Univ Helsinki, Cent Hosp, Dept Radiol, FIN-00029 Helsinki, Finland Univ Helsinki Helsinki Finland FIN-00029 ol, FIN-00029 Helsinki, Finland Univ Kuopio, Dept Clin Radiol, FIN-70211 Kuopio, Finland Univ Kuopio Kuopio Finland FIN-70211 n Radiol, FIN-70211 Kuopio, Finland Univ Helsinki, Cent Hosp, Dept Lab Med, FIN-00029 Helsinki, Finland Univ Helsinki Helsinki Finland FIN-00029 ed, FIN-00029 Helsinki, Finland Univ Helsinki, Cent Hosp, BioMag Lab, FIN-00029 Helsinki, Finland Univ Helsinki Helsinki Finland FIN-00029 ab, FIN-00029 Helsinki, Finland Tampere Univ Technol, Signal Proc Lab, FIN-33101 Tampere, Finland Tampere Univ Technol Tampere Finland FIN-33101 IN-33101 Tampere, Finland
Titolo Testata:
IEEE TRANSACTIONS ON MEDICAL IMAGING
fascicolo: 5, volume: 20, anno: 2001,
pagine: 403 - 414
SICI:
0278-0062(200105)20:5<403:CCFAOF>2.0.ZU;2-U
Fonte:
ISI
Lingua:
ENG
Soggetto:
FMRI TIME-SERIES; HUMAN VISUAL-CORTEX; SIGNIFICANT ACTIVATION; PRINCIPAL COMPONENTS; STATISTICAL-ANALYSIS; IMAGES; BRAIN; SEGMENTATION; QUANTIFICATION; RESTORATION;
Keywords:
clustering; functional magnetic resonance imaging (fMRI); hypothesis testing; segmentation; statistical parametric map;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Engineering, Computing & Technology
Citazioni:
41
Recensione:
Indirizzi per estratti:
Indirizzo: Salli, E Helsinki Univ Technol, Biomed Engn Lab, POB 2200, FIN-02015 Espoo, Finland Helsinki Univ Technol POB 2200 Espoo Finland FIN-02015 , Finland
Citazione:
E. Salli et al., "Contextual clustering for analysis of functional MRI data", IEEE MED IM, 20(5), 2001, pp. 403-414

Abstract

We present a contextual clustering procedure for statistical parametric maps (SPM) calculated from time varying three-dimensional images, The algorithm can be used for the detection of neural activations from functional magnetic resonance images (fMRI). An important characteristic of SPM is that the intensity distribution of background (nonactive area) is known whereas the distributions of activation areas are not, The developed contextual clustering algorithm divides an SPM into background and activation areas so thatthe probability of detecting false activations by chance is controlled, i.e., hypothesis testing is performed. Unlike the much used voxel-by-voxel testing, neighborhood information is utilized, an important difference. This is achieved by using a Markov random field prior and iterated conditional modes (ICM) algorithm. However, unlike in the conventional use of ICM algorithm, the classification is based only on the distribution of background. The results from our simulations and human fMRI experiments using visual stimulation demonstrate that a better sensitivity is achieved with a given specificity in comparison to the voxel-by-voxel thresholding technique. The algorithm is computationally efficient and can be used to detect and delineateobjects from a noisy background in other applications.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 08/04/20 alle ore 09:12:33