De-noising of Low Dose CT Liver Images Using Improved Discrete Wavelet Transform

CT scans have become increasingly common in recent years and are essential for diagnosing various medical conditions. But the most significant element influencing a CT image’s quality is noise, which frequently taints low-dose scans. The radiologists’ choice may then be impacted by this. Therefore, in order to enhance visibility and increase the clarity of the image, LDCT images must be removed of noise before being utilized for diagnosis. This work is one such attempt to remove noise from low dose Computed Tomography (CT) liver images. In the present work, the noise from the CT liver images is eliminated using a modified discrete wavelet transform (DWT). The scheme system is used in conjunction with the DWT. The lifting approach improves the wavelet transform’s accuracy, simplicity, efficiency, and flexibility. Furthermore, the noisy wavelet coefficients are denoised via Bayesian shrinkage. The de-noised image PSNR, MSE, SSIM, and SNR values are computed, and a comparison with the traditional de-noising methods is carried out. The quantitative study reveals that the suggested approach has produced noteworthy outcomes when compared to the efficacy of conventional procedures. Furthermore, as the results and discussion section demonstrate, the suggested lifting technique in conjunction with the DWT minimizes the memory needs while simultaneously reducing complexity.