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Titolo:
Segmentation of medical images using LEGION
Autore:
Shareef, N; Wang, DL; Yagel, R;
Indirizzi:
Ohio State Univ, Dept Comp & Informat Sci, Columbus, OH 43210 USA Ohio State Univ Columbus OH USA 43210 nformat Sci, Columbus, OH 43210 USA Ohio State Univ, Ctr Cognit Sci, Columbus, OH 43210 USA Ohio State Univ Columbus OH USA 43210 Cognit Sci, Columbus, OH 43210 USA
Titolo Testata:
IEEE TRANSACTIONS ON MEDICAL IMAGING
fascicolo: 1, volume: 18, anno: 1999,
pagine: 74 - 91
SICI:
0278-0062(199901)18:1<74:SOMIUL>2.0.ZU;2-J
Fonte:
ISI
Lingua:
ENG
Soggetto:
NEURAL OSCILLATORS; VISUAL-CORTEX; MR-IMAGES; NETWORK; BRAIN; HEAD; CAT;
Keywords:
LEGION; medical images; oscillatory correlation; segmentation;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Engineering, Computing & Technology
Citazioni:
35
Recensione:
Indirizzi per estratti:
Indirizzo: Wang, DL Ohio State Univ, Dept Comp & Informat Sci, Columbus, OH 43210 USAOhio State Univ Columbus OH USA 43210 ci, Columbus, OH 43210 USA
Citazione:
N. Shareef et al., "Segmentation of medical images using LEGION", IEEE MED IM, 18(1), 1999, pp. 74-91

Abstract

Advances in visualization technology and specialized graphic workstations allow clinicians to virtually interact with anatomical structures containedwithin sampled medical-image datasets, A hindrance to the effective use ofthis technology is the difficult problem of image segmentation. In this paper, we utilize a recently proposed oscillator network called the locally excitatory globally inhibitory oscillator network (LEGION) whose ability tb achieve fast synchrony with local excitation and desynchrony with global inhibition makes it an effective computational framework for grouping similarfeatures and segregating dissimilar ones in an image, We extract an algorithm from LEGION dynamics and propose an adaptive scheme for grouping. We show results of the algorithm to two-dimensional (2-D) and three-dimensional (3-D) (volume) computerized topography (CT) and magnetic resonance imaging (MRI) medical-image datasets, In addition, we compare our algorithm with other algorithms for medical-image segmentation, as well as with manual segmentation. LEGION's computational and architectural properties make it a promising approach for real-time medical-image segmentation.

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Documento generato il 16/07/20 alle ore 15:35:09