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Titolo:
Automatic segmentation of dynamic neuroreceptor single-photon emission tomography images using fuzzy clustering
Autore:
Acton, PD; Pilowsky, LS; Kung, HF; Ell, PJ;
Indirizzi:
Univ Penn, Dept Radiol, Philadelphia, PA 19104 USA Univ Penn PhiladelphiaPA USA 19104 pt Radiol, Philadelphia, PA 19104 USA Inst Psychiat, London, England Inst Psychiat London EnglandInst Psychiat, London, England Univ Penn, Dept Pharmacol, Philadelphia, PA 19104 USA Univ Penn Philadelphia PA USA 19104 Pharmacol, Philadelphia, PA 19104 USA Univ Coll London, Sch Med, Inst Nucl Med, London, England Univ Coll London London England Sch Med, Inst Nucl Med, London, England
Titolo Testata:
EUROPEAN JOURNAL OF NUCLEAR MEDICINE
fascicolo: 6, volume: 26, anno: 1999,
pagine: 581 - 590
SICI:
0340-6997(199906)26:6<581:ASODNS>2.0.ZU;2-4
Fonte:
ISI
Lingua:
ENG
Soggetto:
PRINCIPAL COMPONENT ANALYSIS; DOPAMINE D-2 RECEPTORS; HUMAN-BRAIN; I-123 IBZM; H-3 PAROXETINE; MR-IMAGES; BINDING; SPET; PET; CLASSIFICATION;
Keywords:
fuzzy clustering; image segmentation; neuroreceptors, single-photon emission tomography; dopamine receptors;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Life Sciences
Citazioni:
36
Recensione:
Indirizzi per estratti:
Indirizzo: Acton, PD Univ Penn, Dept Radiol, 3700 Mkt St,Room 305, Philadelphia, PA 19104 USA Univ Penn 3700 Mkt St,Room 305 Philadelphia PA USA 19104 104 USA
Citazione:
P.D. Acton et al., "Automatic segmentation of dynamic neuroreceptor single-photon emission tomography images using fuzzy clustering", EUR J NUCL, 26(6), 1999, pp. 581-590

Abstract

The segmentation of medical images is one of the most important steps in the analysis and quantification of imaging data. However, partial volume artefacts make accurate tissue boundary definition difficult, particularly forimages with lower resolution commonly used in nuclear medicine. In single-photon emission tomography (SPET) neuroreceptor studies, areas of specific binding are usually delineated by manually: drawing regions of interest (ROIs), a time-consuming and subjective process. This paper applies the technique off fuzzy c-means clustering (FCM) to automatically seg ment dynamic neuroreceptor SPET images. Fuzzy clustering was tested using a realistic, computer-generated, dynamic SPET phantom derived from segmenting an MR image of an anthropomorphic brain phantom. Also, the utility of applying FCM to real clinical data was assessed by comparison against conventional ROI;analysis of iodine-123 iodobenzamide (IBZM) binding to dopamine D-2/D-3, receptors in the brains of humans.:in addition, a further test of the methodology was assessed: by applying FCM segmentation to [I-123]IDAM images (5-iodo-2-[[2-2-[ (dimethyl amino)methyl] phenyl]thio] benzyl alcohol) of serotonin transporters in non-human primates. In the simulated dynamic SPET phantom, over a wide range of counts and ratios of specific binding to background, FCMcorrelated very strongly with the true counts (correlation coefficient r(2)>0.99, P<0.0001). Similarly, FCM gave segmentation of the [I-123]IBZM datacomparable with manual ROI analysis, with the binding ratios derived from both methods significantly correlated (r(2)=0.83, P<0.0001). Fuzzy clustering is a powerful tool for the automatic, unsupervised segmentation of dynamic neuroreceptor SPET images. Where other automated techniques fail completely, and manual ROI definition would be highly subjective, FCM is capable of segmenting noisy images in a robust and repeatable manner.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 27/01/20 alle ore 17:59:36