title: Disc herniation diagnosis in MRI using a CAD framework and a two-level classifier.
abstract: OBJECTIVE
  Disc herniation in the lumbar spine is a common condition, so an automated method for diagnosis could be helpful in clinical applications. A computer-aided framework for disk herniation diagnosis was developed for use in magnetic resonance imaging (MRI).
  
  
  METHODS
  A computer-aided diagnosis framework for lumbar spine with a two-level classification scheme for disc herniation diagnosis was developed using heterogeneous classifiers: a perceptron classifier, a least mean square classifier, a support vector machine classifier, and a k-Means classifier. Each classifier makes a diagnosis based on a feature set generated from regions of interest that contain vertebrae, a disc, and the spinal cord. Then, an ensemble classifier makes a final decision using score values of each classifier. We used clinical MR image data from 70 subjects in T1-weighted sagittal view and T2-weighted sagittal view for evaluation of the system.
  
  
  RESULTS
  MR images of 70 subjects were processed using the proposed framework resulting in successful detection of disc herniation with 99% accuracy, achieving a speedup factor of 30 in comparison with radiologist's diagnosis.
  
  
  CONCLUSIONS
  The computer-aided framework works well to diagnose herniated discs in MRI scans. We expect the framework can be adapted to effectively diagnose a variety of abnormalities in the lumbar spine.
authors.affiliation: University at Buffalo, SUNY, Buffalo, NY 14260, USA. jkoh@buffalo.edu
authors.forename: Jaehan
authors.initials: J
authors.lastname: Koh
authors.forename: Vipin
authors.initials: V
authors.lastname: Chaudhary
authors.forename: Gurmeet
authors.initials: G
authors.lastname: Dhillon
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007405:Intervertebral Disc Displacement / Q000175:diagnosis*; D016018:Least-Squares Analysis; D008159:Lumbar Vertebrae; D008279:Magnetic Resonance Imaging / Q000379:methods*; D012680:Sensitivity and Specificity; D060388:Support Vector Machine