TY - GEN
T1 - Patch based intensity normalization of brain MR images
AU - Roy, Snehashis
AU - Carass, Aaron
AU - Prince, Jerry L.
PY - 2013
Y1 - 2013
N2 - Magnetic resonance (MR) imaging (MRI) is widely used to study the structure of human brains. Unlike computed tomography (CT), MR image intensities do not have a tissue specific interpretation. Thus images of the same subject obtained with either the same imaging sequence on different scanners or with differing parameters have widely varying intensity scales. This inconsistency introduces errors in segmentation, and other image processing tasks, thus necessitating image intensity standardization. Compared to previous intensity normalization methods using histogram transformations-which try to find a global one-to-one intensity mapping based on histograms-we propose a patch based generative model for intensity normalization between images acquired under different scanners or different pulse sequence parameters. Our method outperforms histogram based methods when normalizing phantoms simulated with various parameters. Additionally, experiments on real data, acquired under a variety of scanners and acquisition parameters, have more consistent segmentations after our normalization.
AB - Magnetic resonance (MR) imaging (MRI) is widely used to study the structure of human brains. Unlike computed tomography (CT), MR image intensities do not have a tissue specific interpretation. Thus images of the same subject obtained with either the same imaging sequence on different scanners or with differing parameters have widely varying intensity scales. This inconsistency introduces errors in segmentation, and other image processing tasks, thus necessitating image intensity standardization. Compared to previous intensity normalization methods using histogram transformations-which try to find a global one-to-one intensity mapping based on histograms-we propose a patch based generative model for intensity normalization between images acquired under different scanners or different pulse sequence parameters. Our method outperforms histogram based methods when normalizing phantoms simulated with various parameters. Additionally, experiments on real data, acquired under a variety of scanners and acquisition parameters, have more consistent segmentations after our normalization.
KW - MRI
KW - brain
KW - intensity normalization
KW - intensity standardization
KW - segmentation
UR - http://www.scopus.com/inward/record.url?scp=84881646761&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84881646761&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2013.6556482
DO - 10.1109/ISBI.2013.6556482
M3 - Conference contribution
C2 - 24443685
AN - SCOPUS:84881646761
SN - 9781467364546
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 342
EP - 345
BT - ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
T2 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
Y2 - 7 April 2013 through 11 April 2013
ER -