title: A modified fuzzy C-means method for segmenting MR images using non-local information.
abstract: BACKGROUND
  In recent years, MR images have been increasingly used in therapeutic applications such as image-guided radiotherapy (IGRT). However, images with low contrast values and noises present challenges for image segmentation.
  
  
  OBJECTIVE
  The objective of this study is to develop a robust method based on fuzzy C-means (FCM) method which can segment MR images polluted with Gaussian noise.
  
  
  METHODS
  A modified FCM algorithm accommodating non-local pixel information via Hausdorff distance was developed for segmenting MR images. The membership and objective functions were modified accordingly. Segmentations with different weights of the Hausdorff distance were compared.
  
  
  RESULTS
  Segmentation tests using synthetic and MR images showed that the proposed algorithm was better at resolving boundaries and more robust to Gaussian noise. By segmenting a sample MR image of a tumor, we further showed the capability of the method in capturing the centroid of the target region.
  
  
  CONCLUSIONS
  The modified FCM algorithm with neighboring information can be used to segment blurry images with potential applications in segmenting motion MR images in image-guided radiotherapy (IGRT).
authors.affiliation: School of Mechanical and Electronic Engineering, Soochow University, Suzhou, Jiangsu, China.|Robotics and Microsystems Center, Soochow University, Suzhou, Jiangsu, China.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Feng
authors.affiliation: School of Mechanical and Electronic Engineering, Soochow University, Suzhou, Jiangsu, China.|Robotics and Microsystems Center, Soochow University, Suzhou, Jiangsu, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Guo
authors.affiliation: School of Mechanical and Electronic Engineering, Soochow University, Suzhou, Jiangsu, China.|Robotics and Microsystems Center, Soochow University, Suzhou, Jiangsu, China.
authors.forename: Hongmiao
authors.initials: H
authors.lastname: Zhang
authors.affiliation: School of Mechanical and Electronic Engineering, Soochow University, Suzhou, Jiangsu, China.|Robotics and Microsystems Center, Soochow University, Suzhou, Jiangsu, China.
authors.forename: Chungang
authors.initials: C
authors.lastname: Li
authors.affiliation: School of Mechanical and Electronic Engineering, Soochow University, Suzhou, Jiangsu, China.|Robotics and Microsystems Center, Soochow University, Suzhou, Jiangsu, China.
authors.forename: Lining
authors.initials: L
authors.lastname: Sun
authors.affiliation: Department of Radiation Oncology, Washington University, St. Louis, MO, USA.
authors.forename: Sasa
authors.initials: S
authors.lastname: Mutic
authors.affiliation: Thayer School of Engineering, Dartmouth College, Hanover, NH, USA.
authors.forename: Songbai
authors.initials: S
authors.lastname: Ji
authors.affiliation: Department of Radiation Oncology, Washington University, St. Louis, MO, USA.|Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, USA.
authors.forename: Yanle
authors.initials: Y
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D059906:Neuroimaging; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: Hausdorff distance; MR images; Segmentation; fuzzy C-means; motion images