@inproceedings{0c928000b90249f18dc4517e208519ec,
title = "Automatic diagnosis of lumbar disc herniation with shape and appearance features from MRI",
abstract = "Intervertebral disc herniation is a major reason for lower back pain (LBP), which is the second most common neurological ailment in the United States. Automation of herniated disc diagnosis reduces the large burden on radiologists who have to diagnose hundreds of cases each day using clinical MRI. We present a method for automatic diagnosis of lumbar disc herniation using appearance and shape features. We jointly use the intensity signal for modeling the appearance of herniated disc and the active shape model for modeling the shape of herniated disc. We utilize a Gibbs distribution for classification of discs using appearance and shape features. We use 33 clinical MRI cases of the lumbar area for training and testing both appearance and shape models. We achieve over 91% accuracy in detection of herniation in a cross-validation experiment with specificity of 91% and sensitivity of 94%.",
keywords = "Computer Aided Diagnosis, Herniation, Lumbar Spine, MRI",
author = "Alomari, {Raja S.} and Corso, {Jason J.} and Vipin Chaudhary and Gurmeet Dhillon",
note = "Publisher Copyright: {\textcopyright} 2010 SPIE.; Medical Imaging 2010: Computer-Aided Diagnosis ; Conference date: 16-02-2010 Through 18-02-2010",
year = "2010",
doi = "10.1117/12.842199",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
editor = "Summers, {Ronald M.} and Nico Karssemeijer",
booktitle = "Medical Imaging 2010",
}