Brain lesion segmentation through physical model estimation

http://link.springer.com/chapter/10.1007/978-3-540-89639-5_54
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Links to Media http://link.springer.com/chapter/10.1007%2F978-3-540-89639-5_54
Publication Type book chapter
Creator Gerig, Guido
Other Author Prastawa, Marcel
Title Brain lesion segmentation through physical model estimation
Date 2008-01-01
Description Segmentations of brain lesions from Magnetic Resonance (MR) images is crucial for quantitative analysis of lesion populations in neuroimaging of neurological disorders. We propose a new method for segmenting lesions in brain MRI by inferring the underlying physical models for pathology. We use the reaction-diffusion model as our physical model, where the diffusion process is guided by real diffusion tensor fields that are obtained from Diffusion Tensor Imaging (DTI). The method performs segmentation by solving the inverse problem, where it determines the optimal parameters for the physical model that generates the observed image. We show that the proposed method can infer reasonable models for multiple sclerosis (MS) lesions and healthy MRI data. The method has potential for further extensions with different physical models or even non-physical models based on existing segmentation schemes.
Type InteractiveResource
Publisher Springer
Journal Title Lecture Notes in Computer Science
Volume 5358
First Page 562
Last Page 571
DOI 10.1007/978-3-540-89639-5_54
Subject Magnetic resonance (MR) images; Neurological disorders; Diffusion tensor imaging (DTI).
Language eng
Bibliographic Citation Prastawa, M., & Gerig, G. (2008). Brain lesion segmentation through physical model estimation. Lecture Notes in Computer Science, 5358, 562-71.
Rights Management (c) Springer (The original publication is available at www.springerlink.com) The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-89639-5_54
Format Medium application/html
Identifier uspace, 19238
ARK ark:/87278/s6pc6bhn
Setname ir_uspace
Date Created 2015-03-06
Date Modified 2015-03-06
ID 712853
Reference URL https://collections.lib.utah.edu/ark:/87278/s6pc6bhn
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