Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains.
Med Image Anal
; 12(6): 639-52, 2008 Dec.
Article
in En
| MEDLINE
| ID: mdl-18440268
ABSTRACT
In the frame of 3D medical imaging, accurate segmentation of multimodal brain MR images is of interest for many brain disorders. However, due to several factors such as noise, imaging artifacts, intrinsic tissue variation and partial volume effects, tissue classification remains a challenging task. In this paper, we present a unifying framework for unsupervised segmentation of multimodal brain MR images including partial volume effect, bias field correction, and information given by a probabilistic atlas. Here-proposed method takes into account neighborhood information using a Hidden Markov Chain (HMC) model. Due to the limited resolution of imaging devices, voxels may be composed of a mixture of different tissue types, this partial volume effect is included to achieve an accurate segmentation of brain tissues. Instead of assigning each voxel to a single tissue class (i.e., hard classification), we compute the relative amount of each pure tissue class in each voxel (mixture estimation). Further, a bias field estimation step is added to the proposed algorithm to correct intensity inhomogeneities. Furthermore, atlas priors were incorporated using probabilistic brain atlas containing prior expectations about the spatial localization of different tissue classes. This atlas is considered as a complementary sensor and the proposed method is extended to multimodal brain MRI without any user-tunable parameter (unsupervised algorithm). To validate this new unifying framework, we present experimental results on both synthetic and real brain images, for which the ground truth is available. Comparison with other often used techniques demonstrates the accuracy and the robustness of this new Markovian segmentation scheme.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Brain
/
Pattern Recognition, Automated
/
Artificial Intelligence
/
Magnetic Resonance Imaging
/
Image Interpretation, Computer-Assisted
/
Image Enhancement
/
Imaging, Three-Dimensional
Type of study:
Diagnostic_studies
/
Evaluation_studies
/
Health_economic_evaluation
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Med Image Anal
Journal subject:
DIAGNOSTICO POR IMAGEM
Year:
2008
Document type:
Article
Affiliation country:
France