Earticle

초록

영어

Brain image segmentation is an important part of medical image analysis. Due to the effect of imaging mechanism, MR images usually intensity in homogeneity, which is also named as bias field. Traditional Gaussian Mixed Model (GMM) method is hard to obtain satisfied segmentation results with the effect of noise and bias field. We propose a novel model based on GMM and nonlocal information. The improved method coupled segmentation and bias field correction that can manage the bias field while segmenting the image. In order to obtain a smooth bias field, we employed the Legendre Polynomials to fit it and merged it to the EM framework. We also use the non local information to deal with the noise and preserve geometrical edges information. The results show that our method can obtain more accurate results and bias field.

목차

Abstract
 1. Introduction
 2. Methods
  2.1. Traditional Gaussian Mixed Model
  2.2. Improved Gaussian Mixed Model
  2.3. Improved GMM based on Non Local Information
 3. Implementation and Results
 4. Conclusions
 Acknowledgements
 References

키워드

저자

• Yunjie Chen [ School of math and statistics, Nanjing University of Information Science & Technology, Nanjing 210044, China ]
• Bo Zhao [ School of math and statistics, Nanjing University of Information Science & Technology, Nanjing 210044, China ]
• Jianwei Zhang [ School of math and statistics, Nanjing University of Information Science & Technology, Nanjing 210044, China ]
• Jin Wang [ School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044, China ]
• Yuhui Zheng [ School of Computer & 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