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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.