Please use this identifier to cite or link to this item:
|Title:||Soft tissue discrimination using magnetic resonance elastography with a new elastic level set model||Authors:||Li, B.N.
level set methods
magnetic resonance elastography (MRE)
medical image segmentation
|Issue Date:||2010||Abstract:||Magnetic resonance elastography (MRE) noninvasively images the propagation of mechanical waves within soft tissues. The elastic properties of soft tissues can then be quantified from MRE wave snapshots. Various algorithms have been proposed to obtain their inversion for soft tissue elasticity. Anomalies are assumed to be discernible in the elasticity map. We propose a new elastic level set model to directly detect and track abnormal soft tissues in MRE wave images. It is derived from the Mumford-Shah functional, and employs partial differential equations for function modeling and smoothing. This level set model can interpret MRE wave images without elasticity reconstruction. The experimental results on synthetic and real MRE wave images confirm its effectiveness for soft tissue discrimination. © 2010 Springer-Verlag Berlin Heidelberg.||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/10635/126695||ISBN:||3642159478||ISSN:||03029743||DOI:||10.1007/978-3-642-15948-0_10|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Aug 19, 2019
checked on Aug 16, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.