|Number of pages||13|
|Early online date||10 Aug 2017|
|Publication status||Published - 01 Nov 2017|
Purpose: Automated and accurate tissue classification in 3D brain Magnetic Resonance images is essential in volumetric morphometry or as a preprocessing step for diagnosing brain diseases. However, noise, intensity inhomogeneity and
partial volume effects limit the classification accuracy of existing methods. This paper provides a comparative study on the contributions of three commonly used image information priors for tissue classification in normal brains: image
intensity, local and multi-atlas priors.
Methods: We compared the effectiveness of the three priors by comparing the four methods modelling them: K-Means (KM), KM combined with a Markov Random Field (KM-MRF), multi-atlas segmentation (MAS) and the combination of KM, MRF and MAS (KM-MRF-MAS). The key parameters and factors in each of the four methods are analysed and the performance of all the models is compared quantitatively and qualitatively on both simulated and real data. Results: The KM-MRF-MAS model that combines the three image information priors performs best.
Conclusions: The image intensity prior is insufficient to generate reasonable results for a few images. Introducing local and multi-atlas priors results in improved brain tissue classification. This study provides a general guide on what
image information priors can be used for effective brain tissue classification.
- classification, MRI, MRF, multi-atlas, prior
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- Comparison of Image Intensity, Local and Multi-Atlas Priors in Brain Tissue Classication
Accepted author manuscript, 862 KB, PDF