Paper
9 March 2010 Automatic diagnosis of lumbar disc herniation with shape and appearance features from MRI
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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%.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Raja' S. Alomari, Jason J. Corso, Vipin Chaudhary, and Gurmeet Dhillon "Automatic diagnosis of lumbar disc herniation with shape and appearance features from MRI", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76241A (9 March 2010); https://doi.org/10.1117/12.842199
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CITATIONS
Cited by 29 scholarly publications.
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KEYWORDS
Magnetic resonance imaging

Computer aided diagnosis and therapy

Binary data

Spine

Diagnostics

Solid modeling

Feature extraction

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