AI Article Synopsis
- The paper introduces a patch-based method for normalizing temporal intensities in longitudinal brain MR images, which is crucial for accurate subsequent analysis like segmentation.
- Instead of focusing on individual voxel intensities, the method uses patches to capture neighborhood information, and models the changes in intensity over time using an auto-regressive process.
- Validation on a phantom dataset shows good segmentation accuracy, while experiments on real data demonstrate improved stability in tissue volume change rates with the normalization applied.
Article Abstract
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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3875181 | PMC |
| http://dx.doi.org/10.1117/12.2006682 | DOI Listing |
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