title: Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm.
abstract: OBJECTIVE
  Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD.
  
  
  METHODS
  In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers.
  
  
  RESULTS
  On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier.
  
  
  CONCLUSIONS
  This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.
authors: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore. aru@np.edu.sg
authors: U Rajendra
authors: UR
authors: Acharya
authors: S Vinitha
authors: SV
authors: Sree
authors: Ricardo
authors: R
authors: Ribeiro
authors: Ganapathy
authors: G
authors: Krishnamurthi
authors: Rui Tato
authors: RT
authors: Marinho
authors: Joao
authors: J
authors: Sanches
authors: Jasjit S
authors: JS
authors: Suri
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D005234:Fatty Liver / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011873:Radiology Information Systems; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014463:Ultrasonography / Q000379:methods*