A survey on noise reduction techniques for lung cancer detection

Lung cancer is one of the fatal diseases that mainly affect the pulmonary nodules of the lungs. Examination of image is presently an essential step of the lung diseases processes like diagnostic, prognostic and follow-up. The survival rate of lung cancer is very low when compared with all other types of cancer. The need for identifying lung cancer at an early stage is very essential and is an active research area in the field of medical image processing. Several Computer aided systems have been intended to distinguish the lung cancer at its initial stage. Various types of images are used for detection of lung diagnosis. The most important challenging task is detection of lung nodule. Computed Tomography (CT) images are generally chosen due to less distortion, low noise, better clarity, less time consumption and low cost. There are different types of the noise present in the images we obtain for the lung mass detection like impulse noise, Gaussian noise and speckle noise. Removal of noise from images is the most active field of research. This paper presents the review on the lung cancer, types of noise in medical imaging and then the methods for the removal of noise.