Medical image denoising system based on stacked convolutional autoencoder for enhancing 2-dimensional gel electrophoresis noise reduction

Abstract

Image denoising is the technique of removing noise or distortions from an image. During medical image acquisition, random noise is added, which results in a lower contrast in those images. For that, image denoising is a crucial task for medical imaging analysis. In this study, a denoising system using three heterogeneous medical datasets is proposed based on stacked convolutional autoencoder (SCAE) technique. To validate its efficiency, different evaluation metrics are used, such as mean squared error (MSE), Peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), structural similarity index measure (SSIM) and cross correlation (CC). The proposed denoising system gives good results among the medical and microscopic datasets that are used for training. The best average results obtained are 0.0039 for MSE, 24.07 for PSNR, 0.1220 for CNR, 0.85 for SSIM, and 0.6358 for CC. Then, the proposed SCAE denoising system was applied to the LECB 2-D PAGE database for denoising real 2-DGE images. The results of denoising 2-DGE images are evaluated by MSE, spot efficiency, false discovery rate (FDR), and signal-to-noise ratio (SNR). The best average results for 2-DGE images are 0.014 for MSE, 75 spot efficiency, 36.3 for FDR and 18.41 for SNR. The proposed system has enhanced the denoising of 2-DGE images by 0.9% to 17.6% when compared to other techniques.