Document Detail


Composite features for automatic diagnosis of intervertebral disc herniation from lumbar MRI.
MedLine Citation:
PMID:  22255478     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
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%.
Authors:
Subarna Ghosh; Raja' S Alomari; Vipin Chaudhary; Gurmeet Dhillon
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference     Volume:  2011     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2011 Aug 
Date Detail:
Created Date:  2012-01-18     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  5068-71     Citation Subset:  IM    
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