Disc herniation diagnosis in MRI using a CAD framework and a two-level classifier

Abstract

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). 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. 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. 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.