Journal article
Semi-supervised maximum a posteriori probability segmentation of brain tissues from dual-echo magnetic resonance scans using incomplete training data
IET Image Processing, Vol.5(3), pp.222-232
2011
Abstract
This study presents a stochastic framework in which incomplete training data are used to boost the accuracy of segmentation and to optimise segmentation when images under consideration are corrupted by inhomogeneities. The authors propose a semi-supervised maximum a posteriori probability (ssMAP) segmentation method that is able to utilise any amount of training data that are usually insufficient for supervised segmentation. The ssMAP unifies supervised and unsupervised segmentation and takes the two as its special cases. To deal with inhomogeneities, the authors propose to incorporate a bias field into the ssMAP and present an algorithm (referred to as ssMAPe) for simultaneous maximum a posteriori probability (MAP) estimation of the inhomogeneity field and segmentation of brain tissues. Experiments on both simulated and real magnetic resonance (MR) images have shown that ssMAP with only a very small quantity of training data improves the segmentation accuracy substantially (up to 30%) compared to both fully supervised and unsupervised methods. The proposed ssMAPe estimates the inhomogeneity field effectively and further improves the segmentation if the MR images are corrupted by inhomogeneity.
Details
- Title
- Semi-supervised maximum a posteriori probability segmentation of brain tissues from dual-echo magnetic resonance scans using incomplete training data
- Authors/Creators
- W. Li (Author/Creator) - University of WollongongP. Ogunbona (Author/Creator) - University of WollongongC. de Silva (Author/Creator)Y. Attikiouzel (Author/Creator) - Murdoch University
- Publication Details
- IET Image Processing, Vol.5(3), pp.222-232
- Publisher
- Institution of Engineering and Technology
- Identifiers
- 991005544534907891
- Copyright
- The Institution of Engineering and Technology 201
- Murdoch Affiliation
- School of Engineering and Energy
- Language
- English
- Resource Type
- Journal article
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Source: InCites
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- Collaboration types
- Domestic collaboration
- Citation topics
- 1 Clinical & Life Sciences
- 1.113 Brain Imaging
- 1.113.460 Advanced Neuroimaging
- Web Of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic
- Imaging Science & Photographic Technology
- ESI research areas
- Engineering