title: Zonal segmentation of prostate using multispectral magnetic resonance images.
abstract: OBJECTIVE
  To investigate the performance of a new method of automatic segmentation of prostatic multispectral magnetic resonance images into two zones: the peripheral zone and the central gland.
  
  
  METHODS
  The proposed method is based on a modified version of the evidential C-means clustering algorithm. The evidential C-means optimization process was modified to introduce spatial neighborhood information. A priori knowledge of the prostate's zonal morphology was modeled as a geometric criterion and used as an additional data source to enhance the differentiation of the two zones.
  
  
  RESULTS
  Thirty-one clinical magnetic resonance imaging series were used to validate the method, and interobserver variability was taken into account in assessing its accuracy. The mean Dice Similarity Coefficient was 89% for the central gland and 80% for the peripheral zone, as validated by a consensus from expert radiologist segmentation.
  
  
  CONCLUSIONS
  The method was statistically insensitive to variations in patient age, prostate volume and the presence of tumors, which increases its feasibility in a clinical context.
authors: Université Nord de France and LAGIS, Villeneuve d'Ascq, 59655, France.
authors: N
authors: N
authors: Makni
authors: A
authors: A
authors: Iancu
authors: O
authors: O
authors: Colot
authors: P
authors: P
authors: Puech
authors: S
authors: S
authors: Mordon
authors: N
authors: N
authors: Betrouni
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D011467:Prostate; D011471:Prostatic Neoplasms / Q000175:diagnosis