سال انتشار: ۱۳۹۰

محل انتشار: نوزدهمین کنفرانس مهندسی برق ایران

تعداد صفحات: ۵

نویسنده(ها):

Rosita Shishegar – Control and Intelligent Processing Centre of Excellence, School of Electrical and Computer Engineering, Faculty of Engineering, University of Tehran
Hamid Soltanian-Zadeh –
Seyed Reza Moghadasi – Department of Mathematics, Sharif University of Technology

چکیده:

Shape analysis plays an important role in many medical imaging studies. One of the recent shape analysis methods uses the Laplace Beltrami eigenvalues which is alsoused in this paper for global shape comparison of hippocampus of normal subjects and epileptic patients. Popularity of the Laplace Beltrami operator in this field is due to its isometryinvariance which avoids pre-processing steps like mapping, registration, and alignment. In addition, it is capable of revealing fine details in shapes that makes this method a good choice for deformation detecting purposes like epilepsydiagnosis. To examine capability of the proposed method, statistical analysis and two ways of classification, support vector machine (SVM) and finding out of normal range (ONR) subjects, are used. Moreover, to evaluate our classification results, K-fold cross-validation is performed. The best achieved results were true positive rate of 91.9% and false positive rate of 33.3%, yielded by ONR classifiers using 3 selected eigenvalues