Denoising of magnetic resonance images of brain tumor using BT-Autonet

Abstract

While obtaining medical images from sources such as Magnetic Resonance Imaging (MRI), Computed Tomography(CT), and ultrasound, noise is observed within images obtained from real world situations. Often this noise is caused due to vibrations of magnetic coils caused by quick electrical pulses, random thermal motion of protons in the tissue, reverberation and refraction artifacts. Denoising technique is one of the critical aspects in the Computer-aided Diagnosis (CAD) system, since MRI is susceptible to noises like Gaussian, Rician and Rayleigh. Traditional methods for MRI denoising are prone to challenges such as loss of information, loss caused during compression and retention of edge features. Hence, this paper presents a comparative analysis of various image denoising methods and hence, proposes an autoencoder based network Brain Tumor (BT)-Autonet for the removal of noise from brain MRI. Further, the performance analysis of the various denoising approaches is measured using different metrics. The proposed network BT-Autonet for 128 × 128 image dataset achieves a Peak Signal-to-Noise Ratio (PSNR) of 30.788, Mean Square Error(MSE) of 25.179, Structural Similarity Index Measure(SSIM) of 0.9 for Gaussian Dataset. It achieves a PSNR of 27.952, MSE of 23.129, SSIM of 0.861 for Rician Dataset and PSNR of 25.329, MSE of 44.378, SSIM of 0.873 for Rayleigh Dataset with an execution time of 10.5 s for Gaussian Dataset, 11 s for Rician Dataset and 11 s for Rayleigh Dataset. For 256 × 256 image dataset, BT-Autonet achieves a PSNR of 30.452, MSE of 30.036, SSIM of 0.816 for Gaussian Dataset while PSNR of 29.64, MSE of 41.684, SSIM of 0.809 for Rician Dataset and PSNR of 12.818, MSE of 67.219, SSIM 0.279 for Rayleigh Dataset with an execution time of 25 s for Gaussian Dataset, 27 s for Rician Dataset and 26 s for Rayleigh Dataset during the examination. Therefore, the proposed network outperformed the existing models in PSNR, SSIM, MSE and execution time.