NDLI: Support Vector Machine with nonlinear-kernel optimization for lateralization of epileptogenic hippocampus in MR images

Content Provider	IEEE Xplore Digital Library
Author	Hosseini, M.-P. Nazem-Zadeh, M.R. Mahmoudi, F. Hao Ying Soltanian-Zadeh, H.
Copyright Year	2014
Description	Author affiliation: Radiol. Dept., Henry Ford Health Syst., Detroit, MI, USA (Hosseini, M.-P.; Nazem-Zadeh, M.R.; Mahmoudi, F.) \|\| Sch. of Electr. & Comput. Eng., Univ. of Tehran. Tehran, Tehran, Iran (Soltanian-Zadeh, H.) \|\| Dept. of Electr. & Comput. Eng., Wayne State Univ., Detroit, MI, USA (Hao Ying)
Abstract	Surgical treatment is suggested for seizure control in medically intractable epilepsy patients. Detailed pre-surgical evaluation and lateralization using Magnetic Resonance Images (MRI) is expected to result in a successful surgical outcome. In this study, an optimized pattern recognition approach is proposed for lateralization of mesial Temporal Lobe Epilepsy (mTLE) patients using asymmetry of imaging indices of hippocampus. T1-weighted and Fluid-Attenuated Inversion Recovery (FLAIR) images of 76 symptomatic mTLE patients are considered. First, hippocampus is segmented using automatic and manual segmentation methods; then, volumetric and intensity features are extracted from the MR images. A nonlinear Support Vector Machine (SVM) with optimized Gaussian Radial Basis Function (GRBF) kernel is used to classify the imaging features. Using leave-one-out cross validation, this method results in a correct lateralization rate of 82%, a probability of detection for the left side of 0.90 (with false alarm probability of 0.04) and a probability of detection for the right side of 0.69 (with zero false alarm probability). The lateralization results are compared to linear SVM, multi-layer perceptron Artificial Neural Network (ANN), and volumetry and FLAIR asymmetry analysis. This lateralization method is suggested for pre-surgical evaluation using MRI before surgical treatment in mTLE patients. It achieves a more correct lateralization rate and fewer false positives.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	1047
Ending Page	1050
File Size	812033
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6943773
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Hippocampus Feature extraction Image segmentation Manuals Kernel Epilepsy Support Vector Machine (SVM) Magnetic Resonance Imaging (MRI) Lateralization
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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