Image Enhancement Filtering Techniques to Enhancect Images of Lung Cancer

Abstract

Medical images are susceptible to noise and poor contrast. Improving the quality of image using different image enhancement techniques is an important area of image research. In this paper, various image enhancement techniques are applied over lung cancer CT images. Identifying mechanisms to detect lung cancer is very important since men and women are found to be most affected by Lung cancer compared to other types of cancer. As per the report from the World Health Organization, 2.09 million cases of Lung cancer were reported in the year 2018. Early detection and treatment of Lung cancer improve the chances of survival. Thus, this paper aims at applying and reviewing different frequency based Image enhancement techniques like Butterworth filter, Gaussian filter, Gabor filter, fast Fourier transform, and Discrete wavelet transform and finding the best filtering technique used to detect Lung cancer from a sample of Computed Tomography (CT) images. Peak to signal noise ratio is calculated to find the best filter since it is similar to reconstruction quality in human’s perception. The results from experiment analysis shows that Gabor filter has higher PSNR value and it’s the best enhancement technique out of the filters taken under study.