원문정보
초록
영어
Brain image segmentation is one of the most important parts of clinical diagnostic tools. However, accurate segmentation of brain images is a very difficult task due to the noise, inhomogeneity and sometimes deviation in brain images. Wells model incorporates the brain image segmentation and inhomogeneity correction into one framework to overcome influences from the intensity inhomogeneity and obtain good segmentation performance. However, the classical Wells model did not take spatial information into account, so its segmentation results are sensitive to the noise. In order to overcome this limitation, the MRF theory and the nonlocal information are used to construct a nonlocal Markov Random Field. With this nonlocal MRF, the improved Wells method can obtain much better segmentation results. The experimental results show that our method is robust to the noise and is able to simultaneously keep the image edge and slender topological structure very well.
목차
1. Introduction
2. Wells, et al., Method
3. Our Method
3.1. MRF Theory
3.2. Nonlocal-MRF Wells’ Method
3.3. NLMRF-Wells Algorithm
4. Implementation and Results
4.1. Evaluation with Synthetic Data
4.2. Evaluation with Brain Image
4.3. Quantitative Analysis
5. Conclusions
Acknowledgments
References