Seyed Mehdi Moghaddasi - Department of Bioelectric Engineering, SRBIAU, Tehran, Iran
Elnaz Mohseni - Department of Bioelectric Engineering, IAUCTB, Tehran, Iran

Noise elimination from images is one of the most important fields of image processing Wavelet-based methods are always associated with Thresholding that are presented togaussian noises elimination. Multiplicity wavelets have properties such as symmetry, highlevelapproximation simultaneously and the image decomposes more accurately and retainsthe edges. In this paper, after magnetic resonance imaging (MRI) decomposition, usingteaching-learning-based optimization (TLBO), appropriate thresholding is used. The TLBOalgorithm is a population-based algorithm inspired by the impact that a teacher has on hislearners. Using the teaching-learning-based optimization algorithm to calculate theappropriate threshold, noise elimination methods increase. Simulation results show that bycalculating the appropriate threshold using TLBO algorithm, multiply wavelet transform isbetter than other methods.

Magnetic resonance imaging, Teaching-learning based optimization, Noise elimination