TY - GEN
T1 - Probabilistic segmentation of brain tumors based on multi-modality magnetic resonance images
AU - Cai, Hongmin
AU - Verma, Ragini
AU - Ou, Yangming
AU - Lee, Seung Koo
AU - Melhem, Elias R.
AU - Davatzikos, Christos
PY - 2007
Y1 - 2007
N2 - In this paper, multi-modal Magnetic Resonance (MR) images are integrated into a tissue profile that aims at differentiating tumor components, edema and normal tissue. This is achieved by a tissue classification technique that learns the appearance models of different tissue types based on training samples identified by an expert and assigns tissue labels to each voxel. These tissue classifiers produce probabilistic tissue maps reflecting imaging characteristics of tumors and surrounding tissues that may be employed to aid in diagnosis, tumor boundary delineation, surgery and treatment planning. The main contributions of this work are: 1) conventional structural MR modalities are combined with diffusion tensor imaging data to create an integrated multimodality profile for brain tumors, and 2) in addition to the tumor components of enhancing and non-enhancing tumor types, edema is also characterized as a separate class in our framework. Classification performance is tested on 22 diverse tumor cases using cross-validation.
AB - In this paper, multi-modal Magnetic Resonance (MR) images are integrated into a tissue profile that aims at differentiating tumor components, edema and normal tissue. This is achieved by a tissue classification technique that learns the appearance models of different tissue types based on training samples identified by an expert and assigns tissue labels to each voxel. These tissue classifiers produce probabilistic tissue maps reflecting imaging characteristics of tumors and surrounding tissues that may be employed to aid in diagnosis, tumor boundary delineation, surgery and treatment planning. The main contributions of this work are: 1) conventional structural MR modalities are combined with diffusion tensor imaging data to create an integrated multimodality profile for brain tumors, and 2) in addition to the tumor components of enhancing and non-enhancing tumor types, edema is also characterized as a separate class in our framework. Classification performance is tested on 22 diverse tumor cases using cross-validation.
KW - Brain tumors
KW - Edema
KW - Multimodal MRI data
KW - Tissue classification
UR - http://www.scopus.com/inward/record.url?scp=36349004706&partnerID=8YFLogxK
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U2 - 10.1109/ISBI.2007.356923
DO - 10.1109/ISBI.2007.356923
M3 - Conference contribution
AN - SCOPUS:36349004706
SN - 1424406722
SN - 9781424406722
T3 - 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings
SP - 600
EP - 603
BT - 2007 4th IEEE International Symposium on Biomedical Imaging
T2 - 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07
Y2 - 12 April 2007 through 15 April 2007
ER -