Composite features for automatic diagnosis of intervertebral disc herniation from lumbar MRI

Lower back pain is widely prevalent in the world today, and the situation is aggravated due to a shortage of radiologists. Intervertebral disc disorders like desiccation, degeneration and herniation are some of the major causes of lower back pain. In this paper, we propose a robust computer-aided herniation diagnosis system for lumbar MRI by first extracting an approximate Region Of Interest (ROI) for each disc and then using a combination of viable features to produce a highly accurate classifier. We describe the extraction of raw, LBP (Local Binary Patterns), Gabor, GLCM (Gray-Level Co-occurrence Matrix), shape, and intensity features from lumbar SPIR T2-weighted MRI and also present a thorough performance comparison of individual and combined features. We perform 5-fold cross validation experiments on 35 cases and report a very high accuracy of 98.29% using a combination of features. Also, combining the desired features and reducing the dimensionality using LDA, we achieve a high sensitivity (true positive rate) of 98.11%.