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.
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Li, B.N. et al. (2010). Soft Tissue Discrimination Using Magnetic Resonance Elastography with a New Elastic Level Set Model. In: Wang, F., Yan, P., Suzuki, K., Shen, D. (eds) Machine Learning in Medical Imaging. MLMI 2010. Lecture Notes in Computer Science, vol 6357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15948-0_10
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DOI: https://doi.org/10.1007/978-3-642-15948-0_10
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