title: Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator.
abstract: BACKGROUND
  Preoperative characterisation of ovarian masses into benign or malignant is of paramount importance to optimise patient management.
  
  
  OBJECTIVE
  In this study, we developed and validated a computerised model to characterise ovarian masses as benign or malignant.
  
  
  METHODS
  Transvaginal 2D B mode static ultrasound images of 187 ovarian masses with known histological diagnosis were included. Images were first pre-processed and enhanced, and Local Binary Pattern Histograms were then extracted from 2 × 2 blocks of each image. A Support Vector Machine (SVM) was trained using stratified cross validation with randomised sampling. The process was repeated 15 times and in each round 100 images were randomly selected.
  
  
  RESULTS
  The SVM classified the original non-treated static images as benign or malignant masses with an average accuracy of 0.62 (95% CI: 0.59-0.65). This performance significantly improved to an average accuracy of 0.77 (95% CI: 0.75-0.79) when images were pre-processed, enhanced and treated with a Local Binary Pattern operator (mean difference 0.15: 95% 0.11-0.19, p < 0.0001, two-tailed t test).
  
  
  CONCLUSIONS
  We have shown that an SVM can classify static 2D B mode ultrasound images of ovarian masses into benign and malignant categories. The accuracy improves if texture related LBP features extracted from the images are considered.
authors.affiliation: Department of Applied Computing, University of Buckingham, Buckingham, MK18 1EG, U.K.
authors.forename: S
authors.initials: S
authors.lastname: Khazendar
authors.affiliation: Department of Cancer and Surgery, Queen Charlotte's and Chelsea Hospital, Imperial College, London, W12 0HS, U.K.
authors.forename: A
authors.initials: A
authors.lastname: Sayasneh
authors.affiliation: Department of Applied Computing, University of Buckingham, Buckingham, MK18 1EG, U.K.
authors.forename: H
authors.initials: H
authors.lastname: Al-Assam
authors.affiliation: Department of Applied Computing, University of Buckingham, Buckingham, MK18 1EG, U.K.
authors.forename: H
authors.initials: H
authors.lastname: Du
authors.affiliation: KU Leuven Department of Development and Regeneration; Obstetrics and Gynaecology, University Hospitals KU Leuven, Leuven, Belgium.
authors.forename: J
authors.initials: J
authors.lastname: Kaijser
authors.affiliation: Queen Charlotte's and Chelsea Hospital, Imperial College, London, W12 0HS, U.K.
authors.forename: L
authors.initials: L
authors.lastname: Ferrara
authors.affiliation: KU Leuven Department of Development and Regeneration; Obstetrics and Gynaecology, University Hospitals KU Leuven, Leuven, Belgium.
authors.forename: D
authors.initials: D
authors.lastname: Timmerman
authors.affiliation: Department of Applied Computing, University of Buckingham, Buckingham, MK18 1EG, U.K.
authors.forename: S
authors.initials: S
authors.lastname: Jassim
authors.affiliation: Department of Cancer and Surgery, Queen Charlotte's and Chelsea Hospital, Imperial College, London, W12 0HS, U.K. ; KU Leuven Department of Development and Regeneration; Obstetrics and Gynaecology, University Hospitals KU Leuven, Leuven, Belgium. ; Queen Charlotte's and Chelsea Hospital, Imperial College, London, W12 0HS, U.K.
authors.forename: T
authors.initials: T
authors.lastname: Bourne
keywords: Decision support techniques; Support Vector Machines; ovarian cancer; ovarian neoplasm; ultrasonography