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

Content Provider	IEEE Xplore Digital Library
Author	Ghosh, S. Alomari, R.S. Chaudhary, V. Dhillon, G.
Copyright Year	2011
Description	Author affiliation: ProScan Imaging Inc., Williamsville, NY 14221, USA (Dhillon, G.) \|\| Department of Computer Science and Engineering, State University of New York at Buffalo, NY 14260, USA (Ghosh, S.; Alomari, R.S.; Chaudhary, V.)
Abstract	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%.
Starting Page	5068
Ending Page	5071
File Size	532446
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091255
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Sensitivity Accuracy Magnetic resonance imaging Principal component analysis Shape Pain
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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