title: Composite features for automatic diagnosis of intervertebral disc herniation from lumbar MRI.
abstract: Lower back pain is widely prevalent in the world today, and the situation is aggravated due to a shortage of radiologists. Intervertebral disc disorders like desiccation, degeneration and herniation are some of the major causes of lower back pain. In this paper, we propose a robust computer-aided herniation diagnosis system for lumbar MRI by first extracting an approximate Region Of Interest (ROI) for each disc and then using a combination of viable features to produce a highly accurate classifier. We describe the extraction of raw, LBP (Local Binary Patterns), Gabor, GLCM (Gray-Level Co-occurrence Matrix), shape, and intensity features from lumbar SPIR T2-weighted MRI and also present a thorough performance comparison of individual and combined features. We perform 5-fold cross validation experiments on 35 cases and report a very high accuracy of 98.29% using a combination of features. Also, combining the desired features and reducing the dimensionality using LDA, we achieve a high sensitivity (true positive rate) of 98.11%.
authors.affiliation: Department of Computer Science and Engineering, State University of New York at Buffalo, NY 14260, USA.
authors.forename: Subarna
authors.initials: S
authors.lastname: Ghosh
authors.forename: Raja' S
authors.initials: RS
authors.lastname: Alomari
authors.forename: Vipin
authors.initials: V
authors.lastname: Chaudhary
authors.forename: Gurmeet
authors.initials: G
authors.lastname: Dhillon
mesh_terms: D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007405:Intervertebral Disc Displacement / Q000473:pathology*; D008159:Lumbar Vertebrae / Q000473:pathology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity