title: A new automated quantification algorithm for the detection and evaluation of focal liver lesions with contrast-enhanced ultrasound.
abstract: OBJECTIVE
  Detect and classify focal liver lesions (FLLs) from contrast-enhanced ultrasound (CEUS) imaging by means of an automated quantification algorithm.
  
  
  METHODS
  The proposed algorithm employs a sophisticated segmentation method to detect and contour focal lesions from 52 CEUS video sequences (30 benign and 22 malignant). Lesion detection involves wavelet transform zero crossings utilization as an initialization step to the Markov random field model toward the lesion contour extraction. After FLL detection across frames, time intensity curve (TIC) is computed which provides the contrast agents' behavior at all vascular phases with respect to adjacent parenchyma for each patient. From each TIC, eight features were automatically calculated and employed into the support vector machines (SVMs) classification algorithm in the design of the image analysis model.
  
  
  RESULTS
  With regard to FLLs detection accuracy, all lesions detected had an average overlap value of 0.89 ± 0.16 with manual segmentations for all CEUS frame-subsets included in the study. Highest classification accuracy from the SVM model was 90.3%, misdiagnosing three benign and two malignant FLLs with sensitivity and specificity values of 93.1% and 86.9%, respectively.
  
  
  CONCLUSIONS
  The proposed quantification system that employs FLLs detection and classification algorithms may be of value to physicians as a second opinion tool for avoiding unnecessary invasive procedures.
authors.affiliation: Department of Medical Physics, School of Medicine, University of Patras, Rion GR 26504, Greece.
authors.forename: Ilias
authors.initials: I
authors.lastname: Gatos
authors.affiliation: Department of Medical Physics, School of Medicine, University of Patras, Rion GR 26504, Greece.
authors.forename: Stavros
authors.initials: S
authors.lastname: Tsantis
authors.affiliation: Department of Radiology, School of Medicine, University of Patras, Rion GR 26504, Greece.
authors.forename: Stavros
authors.initials: S
authors.lastname: Spiliopoulos
authors.affiliation: Department of Energy Technology Engineering, Technological Education Institute of Athens, Athens 12210, Greece.
authors.forename: Aikaterini
authors.initials: A
authors.lastname: Skouroliakou
authors.affiliation: Diagnostic Echotomography SA, 317C Kifissias Avenue, Kifissia GR 14561, Greece.
authors.forename: Ioannis
authors.initials: I
authors.lastname: Theotokas
authors.affiliation: Diagnostic Echotomography SA, 317C Kifissias Avenue, Kifissia GR 14561, Greece.
authors.forename: Pavlos
authors.initials: P
authors.lastname: Zoumpoulis
authors.affiliation: Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030.
authors.forename: John D
authors.initials: JD
authors.lastname: Hazle
authors.affiliation: Department of Medical Physics, School of Medicine, University of Patras, Rion GR 26504, Greece and Department of Imaging Physics, The University of  Texas MD Anderson Cancer Center, Houston, Texas 77030.
authors.forename: George C
authors.initials: GC
authors.lastname: Kagadis
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D003287:Contrast Media; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D060388:Support Vector Machine; D014463:Ultrasonography; D014741:Video Recording; D058067:Wavelet Analysis; D055815:Young Adult
chemical_list: D003287:Contrast Media