NDLI: Computer-aided diagnosis for lumbar mri using heterogeneous classifiers

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, USA (Dhillon, G.) \|\| Dept. of Comput. Sci. & Eng., SUNY - Univ. at Buffalo, Buffalo, NY, USA (Ghosh, S.; Alomari, R.S.; Chaudhary, V.)
Abstract	In this paper we propose a robust and fully automated lumbar herniation diagnosis system based on clinical MRI data which will not only aid a radiologist to make a decision with increased confidence, but will also reduce the time needed to analyze each case. Our method is based on three steps: 1) We automatically label the five lumbar intervertebral discs in a sagittal MRI slice using a probabilistic model and then extract an ROI for each disc using an Active Shape Model. 2) We generate relevant intensity and texture features from each disc ROI. 3) We construct five different classifiers (SVM, PCA+LDA, PCA+Naive Bayes, PCA+QDA, PCA+SVM) and combine them in a majority voting scheme. We perform 5-fold cross-validation experiments and achieve an accuracy of 94.85%, specificity of 95.9% and sensitivity of 92.45% for 35 clinical cases, i.e. a total of 175 lumbar intervertebral discs.
Starting Page	1179
Ending Page	1182
File Size	352223
Page Count	4
File Format	PDF
ISBN	9781424441273
ISSN	19457928
DOI	10.1109/ISBI.2011.5872612
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Magnetic resonance imaging Feature extraction Accuracy Support vector machines Sensitivity Shape Lumbar herniation CAD Lumbar MRI
Content Type	Text
Resource Type	Article

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