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Proceedings Paper

Computer-aided diagnosis of lumbar stenosis conditions
Author(s): Soontharee Koompairojn; Kathleen Hua; Kien A. Hua; Jintavaree Srisomboon
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Paper Abstract

Computer-aided diagnosis (CAD) systems are indispensable tools for patients' healthcare in modern medicine. Nevertheless, the only fully automatic CAD system available for lumbar stenosis today is for X-ray images. Its performance is limited due to the limitations intrinsic to X-ray images. In this paper, we present a system for magnetic resonance images. It employs a machine learning classification technique to automatically recognize lumbar spine components. Features can then be extracted from these spinal components. Finally, diagnosis is done by applying a Multilayer Perceptron. This classification framework can learn the features of different spinal conditions from the training images. The trained Perceptron can then be applied to diagnose new cases for various spinal conditions. Our experimental studies based on 62 subjects indicate that the proposed system is reliable and significantly better than our older system for X-ray images.

Paper Details

Date Published: 9 March 2010
PDF: 12 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76241C (9 March 2010); doi: 10.1117/12.844545
Show Author Affiliations
Soontharee Koompairojn, Univ. of Central Florida (United States)
Kathleen Hua, Emory Univ. (United States)
Kien A. Hua, Univ. of Central Florida (United States)
Jintavaree Srisomboon, BMA General Hospital (Thailand)


Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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