Abstract
Purpose
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).
Materials and Method
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.
Conclusion
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.
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This work is in part supported by grants from NSF and NYSTAR.
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Koh, J., Chaudhary, V. & Dhillon, G. Disc herniation diagnosis in MRI using a CAD framework and a two-level classifier. Int J CARS 7, 861–869 (2012). https://doi.org/10.1007/s11548-012-0674-9
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DOI: https://doi.org/10.1007/s11548-012-0674-9