Liver is the greatest and largest glandular organ in human body by weight. It is absolutely fundamental to human life as it performs some vital functions such as digestion, metabolism and filtering of possibly harmful bio-chemicals from blood. Due to changing lifestyle and excess consumption of packaged food, liver diseases such as liver cancer are becoming very common. Liver cancer is one of the cancers having very low survival rates. Early detection and proper medication is the key to survival from liver cancer. Many imaging techniques are used to detect liver cancer, with Computed Tomography (CT) images being the most widely used technique. Computer Aided Diagnosis (CAD) involves studying CT scan images to detect liver cancer. Due to noises present in CT images, sometimes the effectiveness of algorithm gets affected. It is therefore important to pre-process CT images before being used as input for cancer detection algorithms. This Paper presents a robust technique for pre-processing CT images to remove any possible noise present in CT images. The evaluation metrics used for this technique are Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Metrics(SSIM). Results obtained are SSIM is .85, PSNR is 57.56 dB, and MSE is 0.13.

10.14704/nq.2022.20.9.NQ44227