Lumbar spinal stenosis CAD from clinical MRM and MRI based on inter- and intra-context features with a two-level classifier

Jaehan Koh; Raja S. Alomari; Vipin Chaudhary; Gurmeet Dhillon M.D.

doi:10.1117/12.878332

4 March 2011 Lumbar spinal stenosis CAD from clinical MRM and MRI based on inter- and intra-context features with a two-level classifier

Jaehan Koh, Raja S. Alomari, Vipin Chaudhary, Gurmeet Dhillon M.D.

Author Affiliations +

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 796304 (2011) https://doi.org/10.1117/12.878332
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

An imaging test has an important role in the diagnosis of lumbar abnormalities since it allows to examine the internal structure of soft tissues and bony elements without the need of an unnecessary surgery and recovery time. For the past decade, among various imaging modalities, magnetic resonance imaging (MRI) has taken the significant part of the clinical evaluation of the lumbar spine. This is mainly due to technological advancements that lead to the improvement of imaging devices in spatial resolution, contrast resolution, and multi-planar capabilities. In addition, noninvasive nature of MRI makes it easy to diagnose many common causes of low back pain such as disc herniation, spinal stenosis, and degenerative disc diseases. In this paper, we propose a method to diagnose lumbar spinal stenosis (LSS), a narrowing of the spinal canal, from magnetic resonance myelography (MRM) images. Our method segments the thecal sac in the preprocessing stage, generates the features based on inter- and intra-context information, and diagnoses lumbar disc stenosis. Experiments with 55 subjects show that our method achieves 91.3% diagnostic accuracy. In the future, we plan to test our method on more subjects.

Citation Download Citation

Jaehan Koh, Raja S. Alomari, Vipin Chaudhary, and Gurmeet Dhillon M.D. "Lumbar spinal stenosis CAD from clinical MRM and MRI based on inter- and intra-context features with a two-level classifier", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796304 (4 March 2011); https://doi.org/10.1117/12.878332

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