A Novel and Robust Approach for Denoising of Computed Tomography Images Using Dual Tree Complex Wavelet Packets and Empirical Mode Decomposition towards Optimization

Computed tomography (CT) images are usually corrupted by several noises from the measurement process complicating the automatic feature extraction and analysis of clinical data. To attain the best possible diagnosis it is very vital that medical images be clear, sharp, and free of noise and artifacts. While the techniques for obtaining digital medical images continue to improve, ensuing in images of higher and higher resolution and quality, noise remains an issue for many medical images. Removing noise in these medical images remains as one of the key challenges in the study of medical imaging. A range of literatures have been developed towards solving the hitch of medical images denoising which is a significant stage in an automatic diagnosis system. In this research paper, we propose a new image denoising technique using Dual Tree Complex Wavelet Packets, Empirical Mode Decomposition and Sobel operator. Here, histon process is used in order to surmount the smoothing filter type and it will not affect the lower dimensions. We have taken into consideration two noises, Gaussian and salt & pepper for proposed technique. The performance of the proposed image denoising technique is assessed on the five CT images for the parameters, PSNR and SDME. For performance assessment studies & comparison, our proposed denoising technique is compared with the existing work in assorted noise levels. From the obtained outcomes, we can conclude that the proposed denoising technique have shown better values for the SDME of 67.8798 and PSNR of 24.841 for salt & pepper noise which is very superior compared to existing methods.