title: Ultrasound-Based Characterization of Prostate Cancer Using Joint Independent Component Analysis.
abstract: OBJECTIVE
  This paper presents the results of a new approach for selection of RF time series features based on joint independent component analysis for in vivo characterization of prostate cancer.
  
  
  METHODS
  We project three sets of RF time series features extracted from the spectrum, fractal dimension, and the wavelet transform of the ultrasound RF data on a space spanned by five joint independent components. Then, we demonstrate that the obtained mixing coefficients from a group of patients can be used to train a classifier, which can be applied to characterize cancerous regions of a test patient.
  
  
  RESULTS
  In a leave-one-patient-out cross validation, an area under receiver operating characteristic curve of 0.93 and classification accuracy of 84% are achieved.
  
  
  CONCLUSIONS
  Ultrasound RF time series can be used to accurately characterize prostate cancer, in vivo without the need for exhaustive search in the feature space.
  
  
  CONCLUSIONS
  We use joint independent component analysis for systematic fusion of multiple sets of RF time series features, within a machine learning framework, to characterize PCa in an in vivo study.
authors.affiliation: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
authors.forename: Farhad
authors.initials: F
authors.lastname: Imani
authors.affiliation: University of British Columbia.
authors.forename: Mahdi
authors.initials: M
authors.lastname: Ramezani
authors.affiliation: University of British Columbia.
authors.forename: Saman
authors.initials: S
authors.lastname: Nouranian
authors.affiliation: Robarts Research Institute, Western University.
authors.forename: Eli
authors.initials: E
authors.lastname: Gibson
authors.affiliation: School of Computing, Queens University.
authors.forename: Amir
authors.initials: A
authors.lastname: Khojaste
authors.affiliation: Robarts Research Institute, Western University.
authors.forename: Mena
authors.initials: M
authors.lastname: Gaed
authors.affiliation: London Health Sciences Centre.
authors.forename: Madeleine
authors.initials: M
authors.lastname: Moussa
authors.affiliation: London Health Sciences Centre.
authors.forename: Jose A
authors.initials: JA
authors.lastname: Gomez
authors.affiliation: London Health Sciences Centre.
authors.forename: Cesare
authors.initials: C
authors.lastname: Romagnoli
authors.affiliation: Kingston General Hospital.
authors.forename: Michael
authors.initials: M
authors.lastname: Leveridge
authors.affiliation: Vancouver General Hospital.
authors.forename: Silvia
authors.initials: S
authors.lastname: Chang
authors.affiliation: Robarts Research Institute, Western University.
authors.forename: Aaron
authors.initials: A
authors.lastname: Fenster
authors.affiliation: Kingston General Hospital.
authors.forename: D Robert
authors.initials: DR
authors.lastname: Siemens
authors.affiliation: Western University.
authors.forename: Aaron D
authors.initials: AD
authors.lastname: Ward
authors.affiliation: School of Computing, Queens University.
authors.forename: Parvin
authors.initials: P
authors.lastname: Mousavi
authors.affiliation: University of British Columbia.
authors.forename: Purang
authors.initials: P
authors.lastname: Abolmaesumi
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D015233:Models, Statistical; D011467:Prostate / Q000000981:diagnostic imaging; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*; D014463:Ultrasonography; D058067:Wavelet Analysis