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Proceedings Paper

Automatic diagnosis of lumbar disc herniation with shape and appearance features from MRI
Author(s): Raja' S. Alomari; Jason J. Corso; Vipin Chaudhary; Gurmeet Dhillon
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Paper 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%.

Paper Details

Date Published: 9 March 2010
PDF: 9 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76241A (9 March 2010); doi: 10.1117/12.842199
Show Author Affiliations
Raja' S. Alomari, Univ. at Buffalo, SUNY (United States)
Jason J. Corso, Univ. at Buffalo, SUNY (United States)
Vipin Chaudhary, Univ. at Buffalo, SUNY (United States)
Gurmeet Dhillon, ProScan Imaging Buffalo (United States)

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

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