Artifact removal techniques for lung CT images in lung cancer detection

Abstract

Lung Cancer in today’s world is one of the major widespread dangerous diseases which is the subject of maximum deaths every year. Accurate detection of lung cancer could boost the endurance rate. Medical image processing has a significant impact on the recognition of lung tumors using Computer Tomography (CT) scan images. Images from a CT scan are widely used because they provide comprehensive imaging of tumor progression inside the lungs. Although different types of noise might be experienced while doing CT scans, producing it a monotonous task for recognizing tumors in the lung. Elimination of noise in CT images is a challenging task for medical diagnoses. The presence of noise in an image is inevitable. Hence reducing noise from the CT scan image is critical for further analysis. Hence various filtering techniques have been used that denoise and enhance the image and help in further evaluation of CT images for accurate lung cancer detection. This paper analyses the noises of different kinds in the CT images and different noise removal techniques which help in improving the accuracy of segmentation and feature extraction as they remove unwanted noise and contribute to the accurate detection of lung cancer. The various filtering methods are analyzed with salt along with pepper noise and speckle noise. The performances of different filters are computed in terms of metrics for evaluation like PSNR, SSIM, MSE, and SNR. The experimental results show that the median filter is more efficient in comparison to other filtering methods in eliminating noises that exist in lung CT images by owning fewer MSE values of 214.8522, high SNR value of 19.36304, SSIM value 0.595997, and high PSNR value of 24.80941.