title: Texture-based classification of focal liver lesions on MRI at 3.0 Tesla: a feasibility study in cysts and hemangiomas.
abstract: OBJECTIVE
  To determine the feasibility of texture analysis for the classification of liver cysts and hemangiomas, on nonenhanced, zero-fill interpolated T1- and T2-weighted MR images.
  
  
  METHODS
  Forty-five patients (26 women and 19 men; mean age, 58.1 +/- 16.9 years) with liver cysts or hemangiomas were enrolled in the study. After exclusion of images with artifacts, T1-weighted images of 42 patients, and T2-weighted images of 39 patients, obtained at 3.0 Tesla (T), were available for further analysis. Texture features derived from the gray-level histogram, co-occurrence and run-length matrix, gradient, autoregressive model, and wavelet transform were calculated. Fisher, probability of classification error and average correlation (POE+ACC), and mutual information coefficients were used to extract subsets of optimized texture features. Linear discriminant analysis (LDA) in combination with k nearest neighbor (k-NN) classification, and k-means clustering, were used for lesion classification.
  
  
  RESULTS
  LDA/k-NN produced misclassification rates of 16-18% on T1-weighted, and 12-18% on T2-weighted images. K-means clustering yielded misclassification rates of 15-23% on T1-weighted, and 15-25% on T2-weighted images.
  
  
  CONCLUSIONS
  Texture-based classification of liver cysts and hemangiomas is feasible on zero-fill interpolated MR images obtained at 3.0T. Further studies are warranted to investigate the value of texture-based classification of other liver lesions, such as hepatocellular and cholangiocellular carcinoma, on MRI.
authors.affiliation: Department of Radiology, MR Center, Medical University of Vienna, Vienna, Austria. marius.mayerhoefer@meduniwien.ac.at
authors.forename: Marius E
authors.initials: ME
authors.lastname: Mayerhoefer
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Schima
authors.forename: Siegfried
authors.initials: S
authors.lastname: Trattnig
authors.forename: Katja
authors.initials: K
authors.lastname: Pinker
authors.forename: Vanessa
authors.initials: V
authors.lastname: Berger-Kulemann
authors.forename: Ahmed
authors.initials: A
authors.lastname: Ba-Ssalamah
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016000:Cluster Analysis; D003430:Cross-Sectional Studies; D003560:Cysts / Q000473:pathology*; D005240:Feasibility Studies; D005260:Female; D006391:Hemangioma / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000473:pathology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D012044:Regression Analysis