Filtering Computed Tomography Images by Using An Adaptive Hybrid Technique

Abstract

Medical images are generally noisy due to the physical mechanisms of the acquisition process. In Computed tomography (CT) scan there is a scope to adapt patient image quality and dose. Reduction in radiation dose (i.e. the amount of X-rays) affects the quality of image and is responsible for image noise in CT. Most of the de-noising algorithms assume additive Gaussian noise. This thesis contains a comparative analysis of a number of de-noising algorithms namely wiener filtering, Average filtering , antistropic filtering ,Bilateral filtering, median filtering ,Wavelet filtering, Total variation filtering and convential antistropic filtering. Then, some quantitative performance metrics like Mean Square Error (MSE), Root Mean Square Error (RMSE), Signal to Noise Ratio (SNR), and Peak Signal to Noise Ratio (PSNR) were computed and compared with the previous filters mentioned, The noise were computed and compared for 3different values 3%,5%and7%. This comparison helps in the assessment of image quality and fidelity; it concludes that the bilateral filtering is the most efficient method in removing Gaussian noise from CT scan images. The proposed method combines the bilateral filter and the wavelet decomposition transform to obtain better results than all the other filters compared.