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13 March 2013 Longitudinal intensity normalization of magnetic resonance images using patches
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Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86691J (2013)
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
This paper presents a patch based method to normalize temporal intensities from longitudinal brain magnetic resonance (MR) images. Longitudinal intensity normalization is relevant for subsequent processing, such as segmentation, so that rates of change of tissue volumes, cortical thickness, or shapes of brain structures becomes stable and smooth over time. Instead of using intensities at each voxel, we use patches as image features as a patch encodes neighborhood information of the center voxel. Once all the time-points of a longitudinal dataset are registered, the longitudinal intensity change at each patch is assumed to follow an auto-regressive (AR(1)) process. An estimate of the normalized intensities of a patch at every time-point are generated from a hidden Markov model, where the hidden states are the unobserved normalized patches and the outputs are the observed patches. A validation study on a phantom dataset shows good segmentation overlap with the truth, and an experiment with real data shows more stable rates of change for tissue volumes with the temporal normalization than without.
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Snehashis Roy, Aaron Carass, and Jerry L. Prince "Longitudinal intensity normalization of magnetic resonance images using patches", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86691J (13 March 2013);

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