TY - GEN
T1 - Multi-parameter segmentation of brain images
AU - Dhawan, Atam P.
AU - D'Alessandro, Brian
PY - 2009
Y1 - 2009
N2 - Recent advances in multi-parameter MR brain imaging has enabled multi-class tissue characterization for better quantitative analysis and understanding brain disorders and pathologies. This paper presents a maximum likelihood based method for multi-class segmentation that utilizes spatio-frequency features obtained from wavelet analysis along with the multi-parameter measurements. Results on MR brain images of a patient with stroke are presented.
AB - Recent advances in multi-parameter MR brain imaging has enabled multi-class tissue characterization for better quantitative analysis and understanding brain disorders and pathologies. This paper presents a maximum likelihood based method for multi-class segmentation that utilizes spatio-frequency features obtained from wavelet analysis along with the multi-parameter measurements. Results on MR brain images of a patient with stroke are presented.
KW - Brain image analysis
KW - Multi-parameter segmentation
KW - Tissue characterization
UR - http://www.scopus.com/inward/record.url?scp=70350241354&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350241354&partnerID=8YFLogxK
U2 - 10.1109/NER.2009.5109273
DO - 10.1109/NER.2009.5109273
M3 - Conference contribution
AN - SCOPUS:70350241354
SN - 9781424420735
T3 - 2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09
SP - 222
EP - 225
BT - 2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09
T2 - 2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09
Y2 - 29 April 2009 through 2 May 2009
ER -