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Robust Fuzzy Segmentation of Magnetic Resonance Images
Bethesda, Maryland March 26-March 27
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CBMS.2001.94170914th IEEE Symposium on Computer-Based ...
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Dzung L. Pham, "Robust Fuzzy Segmentation of Magnetic Resonance Images," Computer-Based Medical Systems, IEEE Symposium on, pp. 0127, 14th IEEE Symposium on Computer-Based Medical Systems (CMBS'01), 2001.
 
 
BibTex
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@article{ 10.1109/CBMS.2001.941709,
author = {Dzung L. Pham},
title = {Robust Fuzzy Segmentation of Magnetic Resonance Images},
journal ={Computer-Based Medical Systems, IEEE Symposium on},
volume = {0},
year = {2001},
isbn = {0-7695-1004-3},
pages = {0127},
doi = {http://doi.ieeecomputersociety.org/10.1109/CBMS.2001.941709},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Computer-Based Medical Systems, IEEE Symposium on
TI - Robust Fuzzy Segmentation of Magnetic Resonance Images
SN - 0-7695-1004-3
SP
EP
A1 - Dzung L. Pham,
PY - 2001
VL - 0
JA - Computer-Based Medical Systems, IEEE Symposium on
ER -
 
Dzung L. Pham, Laboratory of Personality and Cognition, Gerontology Research Center
Abstract: A new approach for robust segmentation of magnetic resonance images is described. The approach is derived from a generalization of the objective function used in Pham and Prince's Adaptive Fuzzy C-means algorithm (AFCM). Within the objective function, an additional constraint is placed on the membership functions that forces them to be spatially smooth. Minimization of this objective function results in an unsupervised fuzzy segmentation algorithm that is robust to both intensity inhomogeniety artifacts as well as noise and other artifacts. The efficacy of the algorithm is demonstrated on simulated magnetic resonance images.
Citation:
Dzung L. Pham, "Robust Fuzzy Segmentation of Magnetic Resonance Images," cbms, pp.0127, 14th IEEE Symposium on Computer-Based Medical Systems (CMBS'01), 2001
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