Mr. Mohammad Esmaeilzadeh Profile

Mohammad Esmaeilzadeh

at Australian National Univ

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Proceedings Article | 24 February 2012 Paper

Mesial temporal lobe epilepsy lateralization using SPHARM-based features of hippocampus and SVM

Mohammad Esmaeilzadeh, Hamid Soltanian-Zadeh, Kourosh Jafari-Khouzani

Proceedings Volume 8314, 83144H (2012) https://doi.org/10.1117/12.911740

KEYWORDS: Shape analysis, Epilepsy, Lithium, Brain, Spherical lenses, Image segmentation, 3D modeling, Feature extraction, Chromium, Diagnostics

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This paper improves the Lateralization (identification of the epileptogenic hippocampus) accuracy in Mesial Temporal Lobe Epilepsy (mTLE). In patients with this kind of epilepsy, usually one of the brain's hippocampi is the focus of the epileptic seizures, and resection of the seizure focus is the ultimate treatment to control or reduce the seizures. Moreover, the epileptogenic hippocampus is prone to shrinkage and deformation; therefore, shape analysis of the hippocampus is advantageous in the preoperative assessment for the Lateralization. The method utilized for shape analysis is the Spherical Harmonics (SPHARM). In this method, the shape of interest is decomposed using a set of bases functions and the obtained coefficients of expansion are the features describing the shape. To perform shape comparison and analysis, some pre- and post-processing steps such as "alignment of different subjects' hippocampi" and the "reduction of feature-space dimension" are required. To this end, first order ellipsoid is used for alignment. For dimension reduction, we propose to keep only the SPHARM coefficients with maximum conformity to the hippocampus shape. Then, using these coefficients of normal and epileptic subjects along with 3D invariants, specific lateralization indices are proposed. Consequently, the 1536 SPHARM coefficients of each subject are summarized into 3 indices, where for each index the negative (positive) value shows that the left (right) hippocampus is deformed (diseased). Employing these indices, the best achieved lateralization accuracy for clustering and classification algorithms are 85% and 92%, respectively. This is a significant improvement compared to the conventional volumetric method.

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