Computer-aided diagnosis of lumbar stenosis conditions

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.