Methods of impulsive noise reduction using image processing

Lung cancer is a malignant disease that predominantly affects any part of the respiratory system and metastasize to the lymph nodes. Smoking is regarded as the principal risk factor for development of lung cancer. The lung cancer is of two types, small cell lung cancers (SCLC) and non-small cell lung cancers (NSCLC). Extent to which the cancer has spread in the body determines its stages and needs to be diagnosed at an early stage which introduces us to medical imaging. Various Computer aided systems have been purposeful in determining the tumors leading to Lung cancer. Prominent way of detecting is by creating its visual representations. Computer-assisted tomography using x-rays termed as Computed Tomography (CT) assists in generating images with less noise and eliminating the superimposition of images. Several types of image ‘noise’ are present such as random noise, statistical noise, electronic noise, structural noise; making it a tedious job for detecting the lung tumors. Dynamic part of the research is the removal of these noises, to obtain a hassle free image. This paper aims at presenting a brief review on lung cancer, various noise involved in medical image processing and technique for evicting the noise.