CT Image Denoising Technique using GA aided Window-based Multiwavelet Transformation and Thresholding with the Incorporation of an Effective Quality Enhancement Method

Denoising the CT images removes noise from the CT images and so makes the disease diagnosis procedure more efficient. The denoised images have a notable level of raise in its PSNR values, ensuring a smoother image for diagnosis purpose. In the previous work, a CT image denoising technique using window-based Multi-wavelet transformation and thresholding has been proposed. The performance of the technique has been improved by Genetic Algorithm (GA)-based window selection methodology. However, in the perspective of diagnosis, the PSNR values have not much significance; instead they rely on the quality of the images in the perspective of medical diagnosis. In this paper, a quality enhancement methodology is proposed to include in the CT image denoising technique using window-based multi-wavelet transformation and thresholding. The methodology is comprised of an edge detection technique based on canny algorithm that is performed on the gradient images so that the images are visualized better for diagnosis. A pair of micro block set is generated from the edge detected image and it is subjected to unsharp filter to obtain a sharper image. The smoothness of the image is improved by applying Gaussian filter to the sharper image. Implementation results are given to demonstrate the superior performance of the proposed quality enhancement technique over various CT images in terms of medical perspective.