Elimination of Noise in CT Images of Lung Cancer using Image Preprocessing Filtering Techniques
The Medical Imaging has emerged in the diagnosis of various chronic diseases especially for cancers in large. In current scenario, people affected by the lung cancer have predominantly increased. Computed Tomography (CT) scan imaging are widely used in detection of lung nodules and cancer. In medical imaging, elimination of noises is a challenging task. In order to overcome this challenge, preprocessing is a crucial task to eliminate the noises in medical imaging. In this paper, image pre-processing techniques like noise filters such as Mean, Median, and Wiener are applied on CT scan images of lung cancer, to segment the image for further analysis of cancer detection. The performances of the different filters applied on CT images were evaluated using image quality assessment metrics such as Mean-Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). The experimental study finds the Median filter is more effective in compare to other filters in removing noises present in CT imaging of Lung cancer by having low MSE values and high PSNR values.