title: Local bone enhancement fuzzy clustering for segmentation of MR trabecular bone images.
abstract: OBJECTIVE
  Segmentation of trabecular bone from magnetic resonance (MR) images is a challenging task due to spatial resolution limitations, signal-to-noise ratio constraints, and signal intensity inhomogeneities. This article examines an alternative approach to trabecular bone segmentation using partial membership segmentation termed fuzzy C-means clustering incorporating local second order features for bone enhancement (BE-FCM) at multiple scales. This approach is meant to allow for a soft segmentation that accounts for partial volume effects while suppressing the influence of noise.
  
  
  METHODS
  A soft segmentation method was developed and evaluated on three different sets of data; interscan reproducibility was evaluated on six test-retest in vivo MR scans of the proximal femur, correlation between MR and HR-pQCT measurements was evaluated on 49 in vivo scans from the distal tibia, and the potential for fracture discrimination was evaluated using MR scans of calcaneus specimens from 15 participants with and 15 participants without vertebral fracture. The algorithm was compared to fuzzy clustering using the intensity as the only feature (I-FCM) and a dual thresholding algorithm. The metric evaluated was bone volume over total volume (BV/TV) within user-defined regions of interest.
  
  
  RESULTS
  BE-FCM had a higher interscan reproducibility (rms CV: 2.0%) compared to I-FCM (5.6%) and thresholding (4.2%), and expressed higher correlation to HR-pQCT data (r = 0.79, p < 10(-11)) compared to I-FCM (r = 0.74, p < 10(-8)) and thresholding (r = 0.70, p < 10(-6)). BE-FCM was also the method that was best able to differentiate between a control and a vertebral fracture group at a 95% significance level.
  
  
  CONCLUSIONS
  The results suggest that trabecular bone segmentation by BE-FCM can provide a precise BV/TV measurement that is sensitive to pathology. The segmentation method may become useful in MR imaging-based quantification of bone microarchitecture.
authors.affiliation: Department of Radiology and Biomedical Imaging, Musculoskeletal and Quantitative Imaging Research Group (MQIR), University of California, San Francisco, California 94158, USA. jenny.folkesson@radiology.ucsf.edu
authors.forename: Jenny
authors.initials: J
authors.lastname: Folkesson
authors.forename: Julio
authors.initials: J
authors.lastname: Carballido-Gamio
authors.forename: Felix
authors.initials: F
authors.lastname: Eckstein
authors.forename: Thomas M
authors.initials: TM
authors.lastname: Link
authors.forename: Sharmila
authors.initials: S
authors.lastname: Majumdar
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001842:Bone and Bones / Q000473:pathology*; D016000:Cluster Analysis; D050723:Fractures, Bone / Q000473:pathology*; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity