title: Statistical shape and texture model of quadrature phase information for prostate segmentation.
abstract: OBJECTIVE
  Prostate volume estimation from segmentation of transrectal ultrasound (TRUS) images aids in diagnosis and treatment of prostate hypertrophy and cancer. Computer-aided accurate and computationally efficient prostate segmentation in TRUS images is a challenging task, owing to low signal-to-noise ratio, speckle noise, calcifications, and heterogeneous intensity distribution in the prostate region.
  
  
  METHODS
  A multi-resolution framework using texture features in a parametric deformable statistical model of shape and appearance was developed to segment the prostate. Local phase information of log-Gabor quadrature filter extracted texture of the prostate region in TRUS images. Large bandwidth of log-Gabor filter ensures easy estimation of local orientations, and zero response for a constant signal provides invariance to gray level shift. This aids in enhanced representation of the underlying texture information of the prostate unaffected by speckle noise and imaging artifacts. The parametric model of the propagating contour is derived from principal component analysis of prior shape and texture information of the prostate from the training data. The parameters were modified using prior knowledge of the optimization space to achieve segmentation.
  
  
  RESULTS
  The proposed method achieves a mean Dice similarity coefficient value of 0.95 ± 0.02 and mean absolute distance of 1.26 ± 0.51 millimeter when validated with 24 TRUS images of 6 data sets in a leave-one-patient-out validation framework.
  
  
  CONCLUSIONS
  The proposed method for prostate TRUS image segmentation is computationally efficient and provides accurate prostate segmentations in the presence of intensity heterogeneities and imaging artifacts.
authors.affiliation: Computer Vision and Robotics Group, University of Girona, Girona, Spain. soumyaghose@gmail.com
authors.forename: Soumya
authors.initials: S
authors.lastname: Ghose
authors.forename: Arnau
authors.initials: A
authors.lastname: Oliver
authors.forename: Robert
authors.initials: R
authors.lastname: Martí
authors.forename: Xavier
authors.initials: X
authors.lastname: Lladó
authors.forename: Jordi
authors.initials: J
authors.lastname: Freixenet
authors.forename: Jhimli
authors.initials: J
authors.lastname: Mitra
authors.forename: Joan C
authors.initials: JC
authors.lastname: Vilanova
authors.forename: Josep
authors.initials: J
authors.lastname: Comet-Batlle
authors.forename: Fabrice
authors.initials: F
authors.lastname: Meriaudeau
mesh_terms: D006801:Humans; D006984:Hypertrophy / Q000000981:diagnostic imaging; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011467:Prostate / Q000000981:diagnostic imaging*; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging; D014463:Ultrasonography