Noise Reduction in Brain Magnetic Resonance Imaging Using a Convolutional Autoencoder

In clinical practice, precise and high-quality brain Magnetic Resonance Imaging (MRI) is pivotal for diagnosing and formulating effective treatment strategies. The research objective is to assess the viability of employing a Convolutional Autoencoders (CAE) for the mitigating noise in brain MRI images. The focus is brain MRI images and the various types of noise (Salt and Pepper, Speckle, and Gaussian noise) that typically corrupt images and may lead to inaccuracies in diagnosis. The research also applies methods to artificially generate these noise types to represent real-world scenarios. Specifically, the dataset of brain MRI images is collected, pre-processed, and artificially exposed to various noise types to simulate the real-world conditions after the CAE model is used to reconstruct the corrupted images. The CAE is assessed for its high efficiency and efficacy using Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). The results indicate that the CAE is very effective in removing noise, particularly Salt and Pepper noise. The model achieves a PSNR of 27.0687 dB and an MSE of 0.00216246 at the lowest noise level. The model also demonstrates stability under varying levels of Speckle noise. Although performance degrades as noise increases, the model continues to demonstrate potential for further refinement. The research furthers the CAE’s analytical potential by assessing its denoising capabilities across various noise types and levels. The research adds value by outlining recommendations to the medical imaging community while identifying the need for future research on different classifications of noise and advanced regularization methods.