Low Dose Computed Tomography (LDCT) scan is modern medical imaging diagnostic technique that provides a detailed projection of internal human body tissue level structures. Even though the LDCT image quality is compromised by Gaussian-noise, which can be generated during image acquisition, this compromises the accurate diagnostic precision. The effective denoising is required to improve image quality in LDCT images. This study demonstrates that the Discrete Wavelet Transform(DWT) method shows better results, both quantitatively and visually, under varying noise intensities ($\sigma=10,20,30,$ and $40$). The DWT method decomposes the image to multiresolution subbands (approximation, and detail) to provide localized analysis of structural patterns. The thresholding method is applied to the detail (noisy) coefficients and then reconstructs the refined image from these denoised coefficients. The DWT method achieved superior noise suppression while preserving edge information. The quantitative analysis among various methods, including PCA, MSVD, DCT, and DWT, consistently shows superior results, achieving a higher PSNR of $33.85$ dB, SNR of $28.50$ dB, and SSIM of $0.7194$ at a noise level $\sigma =10$. Among all denoising methods, the DWT is a powerful and consistent method in image processing to enhance image quality in LDCT images.