Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-15699-1_32
Title: Level set diffusion for MRE image enhancement
Authors: Li, B.N.
Chui, C.K. 
Ong, S.H. 
Chang, S.
Kobayashi, E.
Keywords: Anisotropic diffusion
image enhancement
level set methods
magnetic resonance elastography (MRE)
noise suppression
Issue Date: 2010
Source: Li, B.N.,Chui, C.K.,Ong, S.H.,Chang, S.,Kobayashi, E. (2010). Level set diffusion for MRE image enhancement. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6326 LNCS : 305-313. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-15699-1_32
Abstract: Magnetic resonance elastography (MRE) is an emerging technique for noninvasive imaging of tissue elasticity. Proprietary algorithms are used to reconstruct tissue elasticity from the images of wave propagation within soft tissue. Elasticity reconstruction suffers from interfering noise and outliers. The interference causes biased elasticity and undesired artifacts in the reconstructed elasticity map. Anisotropic geometric diffusion is able to suppress image noise while enhance inherent features. Therefore we integrate anisotropic diffusion with level set methods for numerical enhancement of MRE wave images. Performance evaluation of the proposed level set diffusion (LSD) approach was conducted on both synthetic and real MRE datasets. Experimental results confirm the effectiveness of LSD for MRE image enhancement and direct inversion. © 2010 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/51200
ISBN: 3642156983
ISSN: 03029743
DOI: 10.1007/978-3-642-15699-1_32
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