Automatic Noise Removal in MR Images Using Bilateral Filtering Associated with Artificial Neural Networks
Yu-Ju Lin and Herng-Hua Chang
Computational Biomedical Engineering Laboratory (CBEL)/Department of Engineering Science and Ocean Engineering/National Taiwan University, Daan 10617 Taipei, Taiwan
Abstract—Noise removal in Magnetic Resonance (MR) images is important and essential for a wide variety of subsequent processing applications. Among the abundant denoising algorithms, the bilateral filter has been widely used in many image preprocessing procedures. However, it requires laborious tuning of parameters to obtain optimal filtering results, which is tedious and time-consuming. To address this problem, this paper is in an attempt to automate the bilateral filter based on an artificial neural network. Seven most significant attributes among 60 image attributes are used as the network input arguments. The BrainWeb image data with various scenarios of noise level, intensity non-uniformity, and slice thickness were adopted to evaluate this new system. Experimental results indicated that our automatic bilateral filter accurately predicted the denoising parameters and effectively removed the noise in MR images.
Index Terms—bilateral filter, MRI, neural networks, denoise, Automation
Cite: Yu-Ju Lin and Herng-Hua Chang, "Automatic Noise Removal in MR Images Using Bilateral Filtering Associated with Artificial Neural Networks," International Journal of Pharma Medicine and Biological Sciences, Vol. 4, No. 1, pp. 39-43, January 2015.