title: Segmentation of MR image using local and global region based geodesic model.
abstract: BACKGROUND
  Segmentation of the magnetic resonance (MR) images is fundamentally important in medical image analysis. Intensity inhomogeneity due to the unknown noise and weak boundary makes it a difficult problem.
  
  
  METHODS
  The paper presents a novel level set geodesic model which integrates the local and the global intensity information in the signed pressure force (SPF) function to suppress the intensity inhomogeneity and implement the segmentation. First, a new local and global region based SPF function is proposed to extract the local and global image information in order to ensure a flexible initialization of the object contours. Second, the global SPF is adaptively balanced by the weight calculated by using the local image contrast. Third, two-phase level set formulation is extended to a multi-phase formulation to successfully segment brain MR images.
  
  
  RESULTS
  Experimental results on the synthetic images and MR images demonstrate that the proposed method is very robust and efficient. Compared with the related methods, our method is much more computationally efficient and much less sensitive to the initial contour. Furthermore, the validation on 18 T1-weighted brain MR images (International Brain Segmentation Repository) shows that our method can produce very promising results.
  
  
  CONCLUSIONS
  A novel segmentation model by incorporating the local and global information into the original GAC model is proposed. The proposed model is suitable for the segmentation of the inhomogeneous MR images and allows flexible initialization.
authors.affiliation: Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, PR China. mxiul305@163.com.|Department of Anatomy, Histology and Embryology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, PR China. mxiul305@163.com.|Shanghai Key Laboratory of Medical Imaging Computing and Computer-Assisted Intervention, Shanghai, 200032, PR China. mxiul305@163.com.
authors.forename: Xiuming
authors.initials: X
authors.lastname: Li
authors.affiliation: Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, PR China. dongshengjiang@fudan.edu.cn.|Shanghai Key Laboratory of Medical Imaging Computing and Computer-Assisted Intervention, Shanghai, 200032, PR China. dongshengjiang@fudan.edu.cn.
authors.forename: Dongsheng
authors.initials: D
authors.lastname: Jiang
authors.affiliation: Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, PR China. yonghong.shi@fudan.edu.cn.|Shanghai Key Laboratory of Medical Imaging Computing and Computer-Assisted Intervention, Shanghai, 200032, PR China. yonghong.shi@fudan.edu.cn.
authors.forename: Yonghong
authors.initials: Y
authors.lastname: Shi
authors.affiliation: Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, PR China. wshengli88@shmu.edu.cn.|Department of Anatomy, Histology and Embryology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, PR China. wshengli88@shmu.edu.cn.|Shanghai Key Laboratory of Medical Imaging Computing and Computer-Assisted Intervention, Shanghai, 200032, PR China. wshengli88@shmu.edu.cn.
authors.forename: Wensheng
authors.initials: W
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D006225:Hand / Q000098:blood supply; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods; D019047:Phantoms, Imaging