Earticle

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

Abstract
 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

키워드

저자

• Zhongyuan Cui [ School of Computer Science and Technology & School of Software, Zhoukou Normal University, Zhoukou 466001, China ]
• Feng Wang [ School of Computer Science and Technology & School of Software, Zhoukou Normal University, Zhoukou 466001, China ]
• Jin Wang [ School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605