title: Complex threshold method for identifying pixels that contain predominantly noise in magnetic resonance images.
abstract: OBJECTIVE
  To create a robust means to remove noise pixels using complex data.
  
  
  METHODS
  A receiver operating characteristic (ROC) curve was used to determine the appropriate choice of magnitude and phase thresholds as well as connectivity values to determine what pixels represent noise in the image. To fine-tune the results, a spike removal and hole replacement operator is applied to reduce Type I error and remove small islands of noise.
  
  
  RESULTS
  The use of phase information improves the magnitude-only thresholding approach by further recognizing pixels that contain only noise. The performance of the method is enhanced using local connectivity of magnitude and phase data. An ROC analysis on simulated data shows that the Type I and Type II errors are less than 10(-4) and 10(-3), respectively, without connectivity and 0 and 10(-3), respectively, with connectivity for a signal-to-noise ratio (SNR) of 3:1 or higher.
  
  
  CONCLUSIONS
  The joint use of both magnitude and phase images helps to improve the removal of noise points in magnetic resonance images. This can prove useful in automating the visualization of phase images without the highly distractive phase noise in noise regions. Also, it is useful in susceptibility weighted imaging when taking the minimum intensity projections of variably sized regions.
authors.affiliation: Department of Computer Science, Wayne State University, Detroit, Michigan 48201, USA.
authors.forename: Daniel S J
authors.initials: DS
authors.lastname: Pandian
authors.forename: Carlo
authors.initials: C
authors.lastname: Ciulla
authors.forename: E Mark
authors.initials: EM
authors.lastname: Haacke
authors.forename: Jing
authors.initials: J
authors.lastname: Jiang
authors.forename: Muhammad
authors.initials: M
authors.lastname: Ayaz
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted