title: Novel segmentation method for abdominal fat quantification by MRI.
abstract: OBJECTIVE
  To introduce and describe the feasibility of a novel method for abdominal fat segmentation on both water-saturated and non-water-saturated MR images with improved absolute fat tissue quantification.
  
  
  METHODS
  A general fat distribution model which fits both water-saturated (WS) and non-water-saturated (NWS) MR images based on image gray-level histogram is first proposed. Next, a novel fuzzy c-means clustering step followed by a simple thresholding is proposed to achieve automated and accurate abdominal quantification taking into consideration the partial-volume effects (PVE) in abdominal MR images. Eleven subjects were scanned at central abdomen levels with both WS and NWS MRI techniques. Synthesized "noisy" NWS (nNWS) images were also generated to study the impact of reduced SNR on fat quantification using the novel approach. The visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) amounts of the WS MR images were quantified with a traditional intensity thresholding method as a reference to evaluate the performance of the novel method on WS, NWS, and nNWS MR images.
  
  
  RESULTS
  The novel approach resulted in consistent SAT and VAT amounts for WS, NWS, and nNWS images. Automatic segmentation and incorporation of spatial information during segmentation improved speed and accuracy. These results were in good agreement with those from the WS images quantified with a traditional intensity thresholding method and accounted for PVE contributions.
  
  
  CONCLUSIONS
  The proposed method using a novel fuzzy c-means clustering method followed by thresholding can achieve consistent quantitative results on both WS and NWS abdominal MR images while accounting for PVE contributing inaccuracies.
authors.affiliation: Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.
authors.forename: Anqi
authors.initials: A
authors.lastname: Zhou
authors.forename: Horacio
authors.initials: H
authors.lastname: Murillo
authors.forename: Qi
authors.initials: Q
authors.lastname: Peng
mesh_terms: D050153:Abdominal Fat / Q000033:anatomy & histology*; D000273:Adipose Tissue / Q000033:anatomy & histology / Q000378:metabolism; D000704:Analysis of Variance; D001823:Body Composition; D005240:Feasibility Studies; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009765:Obesity / Q000175:diagnosis; D015203:Reproducibility of Results; D012494:Sampling Studies; D014867:Water
chemical_list: D014867:Water