title: Pattern recognition system for focal liver lesions using "crisp" and "fuzzy" classifiers.
abstract: OBJECTIVE
  To determine the diagnostic performance of an artificial intelligence system for classification of focal liver lesions, in comparison to human observers.
  
  
  METHODS
  One hundred forty-three focal hepatic lesions were evaluated with dynamic computed tomography. The study comprised 59 hemangiomas, 24 other benign lesions (focal nodular hyperplasia, adenoma), and 60 malignant liver lesions (18 primary, 42 secondary). All lesions but the hemangiomas were histologically examined by needle biopsy. For delineation of the lesion, a region of interest was defined interactively. The pattern recognition was performed in two steps with initial extraction of textural features: training of a classifier and classification of the lesions. The accuracy of classification of hepatic lesions into three groups (hemangioma, other benign processes, malignant lesions) was tested. The results were compared with those achieved by human observers using receiver operating characteristic statistical analysis.
  
  
  RESULTS
  The accuracy (total rate of correct diagnoses) was 90.2%. False classifications were found owing to small size, weak contrast enhancement after bolus injection, respiratory movement, and atypical morphology of the lesion. The area under the receiver operating characteristic curve was not significantly different for computer and human observers.
  
  
  CONCLUSIONS
  The system demonstrated a diagnostic accuracy comparable to human observers. Further improvement with increasing numbers of typical computed tomographic series for training of the classifier can be expected.
authors.affiliation: Diagnostic Radiology, RWTH Aachen, Germany.
authors.forename: H M
authors.initials: HM
authors.lastname: Klein
authors.forename: T
authors.initials: T
authors.lastname: Eisele
authors.forename: K C
authors.initials: KC
authors.lastname: Klose
authors.forename: I
authors.initials: I
authors.lastname: Stauss
authors.forename: M
authors.initials: M
authors.lastname: Brenner
authors.forename: W
authors.initials: W
authors.lastname: Ameling
authors.forename: R W
authors.initials: RW
authors.lastname: Günther
mesh_terms: D000236:Adenoma / Q000000981:diagnostic imaging / Q000473:pathology; D016477:Artifacts; D001185:Artificial Intelligence; D001707:Biopsy, Needle; D003287:Contrast Media; D017143:Fuzzy Logic; D006391:Hemangioma / Q000000981:diagnostic imaging / Q000473:pathology; D006801:Humans; D006965:Hyperplasia; D008099:Liver / Q000000981:diagnostic imaging / Q000473:pathology; D008107:Liver Diseases / Q000145:classification / Q000000981:diagnostic imaging* / Q000473:pathology; D008113:Liver Neoplasms / Q000145:classification / Q000000981:diagnostic imaging / Q000473:pathology; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D011856:Radiographic Image Enhancement; D014057:Tomography, X-Ray Computed / Q000379:methods
chemical_list: D003287:Contrast Media