Elimination of white Gaussian noise in arterial phase CT images to bring adrenal tumours into the forefront.

Dynamic Contrast-Enhanced Computed Tomography (DCE-CT) is applied to observe adrenal tumours in detail by utilising from the contrast matter, which generally brings the tumour into the forefront. However, DCE-CT images are generally influenced by noises that occur as the result of the trade-off between radiation doses vs. noise. Herein, this situation constitutes a challenge in the achievement of accurate tumour segmentation. In CT images, most of the noises are similar to Gaussian Noise. In this study, arterial phase CT images containing adrenal tumours are utilised, and elimination of Gaussian Noise is realised by fourteen different techniques reported in literature for the achievement of the best denoising process. In this study, the Block Matching and 3D Filtering (BM3D) algorithm typically achieve reliable Peak Signal-to-Noise Ratios (PSNR) and resolves challenges of similar techniques when addressing different levels of noise. Furthermore, BM3D obtains the best mean PSNR values among the first five techniques. BM3D outperforms to other techniques by obtaining better Total Statistical Success (TSS), CPU time and computation cost. Consequently, it prepares clearer arterial phase CT images for the next step (segmentation of adrenal tumours).