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Titolo:
Tissue classification based on 3D local intensity structures for volume rendering
Autore:
Sato, Y; Westin, CF; Bhalerao, A; Nakajima, S; Shiraga, N; Tamura, S; Kikinis, R;
Indirizzi:
Osaka Univ, Grad Sch Med, Biomed Res Ctr, Div Funct Diagnost Imaging, Suita, Osaka 5650871, Japan Osaka Univ Suita Osaka Japan 5650871 Imaging, Suita, Osaka 5650871, Japan Harvard Univ, Sch Med, Brigham & Womens Hosp, Dept Radiol, Boston, MA 02115 USA Harvard Univ Boston MA USA 02115 Hosp, Dept Radiol, Boston, MA 02115 USA Univ Warwick, Dept Comp Sci, Coventry CV4 7AL, W Midlands, England Univ Warwick Coventry W Midlands England CV4 7AL 7AL, W Midlands, England Nakakawachi Med Ctr Acute Med, Osaka 5780947, Japan Nakakawachi Med Ctr Acute Med Osaka Japan 5780947 , Osaka 5780947, Japan Keio Univ, Sch Med, Dept Diagnost Radiol, Shinjuku Ku, Tokyo 1608582, Japan Keio Univ Tokyo Japan 1608582 Radiol, Shinjuku Ku, Tokyo 1608582, Japan
Titolo Testata:
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
fascicolo: 2, volume: 6, anno: 2000,
pagine: 160 - 180
SICI:
1077-2626(200004/06)6:2<160:TCBO3L>2.0.ZU;2-D
Fonte:
ISI
Lingua:
ENG
Soggetto:
MR-ANGIOGRAPHY; IMAGES; CT;
Keywords:
volume visualization; image enhancement; medical image; 3D derivative feature; multiscale analysis; multidimensional opacity function; multichannel classification; partial volume effect;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
39
Recensione:
Indirizzi per estratti:
Indirizzo: Sato, Y Osaka Univ, Grad Sch Med, Biomed Res Ctr, Div Funct Diagnost Imaging, RoomD11,2-2 Yamada Oka, Suita, Osaka 5650871, Japan Osaka Univ Room D11,2-2 Yamada Oka Suita Osaka Japan 5650871 Japan
Citazione:
Y. Sato et al., "Tissue classification based on 3D local intensity structures for volume rendering", IEEE VIS C, 6(2), 2000, pp. 160-180

Abstract

This paper describes a novel approach to tissue classification using three-dimensional (3D) derivative features in the volume rendering pipeline. In conventional tissue classification for a scalar volume, tissues of interestare characterized by an opacity transfer function defined as a one-dimensional (1D) function of the original volume intensity. To overcome the limitations inherent in conventional 1D opacity functions, we propose a tissue classification method that employs a multidimensional opacity function, whichis a function of the 3D derivative features calculated from a scalar volume as well as the volume intensity. Tissues of interest are characterized byexplicitly defined classification rules based on 3D filter responses highlighting local structures, such as edge, sheet, line, and blob. which typically correspond to tissue boundaries, cortices, vessels, and nodules, respectively, in medical volume data. The 3D local structure filters are formulated using the gradient vector and Hessian matrix of the volume intensity function combined with isotropic Gaussian blurring. These filter responses andthe original intensity define a multidimensional feature space in which multichannel tissue classification strategies are designed. The usefulness ofthe proposed method is demonstrated by comparisons with conventional single-channel classification using both synthesized data and clinical data acquired with CT (computed tomography) and MRI (magnetic resonance imaging) scanners. The improvement in image quality obtained using multichannel classification is confirmed by evaluating the contrast and contrast-to-noise ratioin the resultant volume-rendered images with variable opacity values.

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Documento generato il 03/04/20 alle ore 19:42:43