Elimination of white Gaussian noise in arterial phase CT images to bring adrenal tumours into the forefront. (April 2018)

• Record Type: Journal Article
• Authors: Koyuncu, Hasan; Ceylan, Rahime
• Is Part Of: Computerized medical imaging and graphics. Volume 65(2018)
• Page Start: 46
• Page End: 57
• Publication Date: 2018-04
• Subjects: Image denoising -- White Gaussian noise -- Adrenal tumours -- Arterial phase -- Contrast-enhanced CT; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2017.05.004
• Languages: English
• ISSNs: 0895-6111

Abstract

Graphical abstract: Highlights: DCE-CT images are generally under the influence of noises which arise as the result of trade-off between radiation doses vs. noise. This situation constitutes a challenging handicap for the achievement of an accurate tumour segmentation. In this study, arterial phase CT images containing adrenal tumours is handled and elimination of Gaussian Noise is realised by fourteen different techniques. In experiments, it's revealed that BM3D algorithm usually achieves to reliable PSNR values by outperforming to peer techniques on denoising of abdominal CT image. As a result, the whole abdomen and the ROI around adrenal tumours came to the forefront for our future work. Abstract: 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).