In the medical field, Image processing methods are widely used. It is a method for the improvement of image, the image which is obtained after processing is useful for earlier detection and various stages of cancer. In cancer tumors such as lung cancer time factor is the important key point because in the targated images of lung cancer time factor is use to discover the abnormality. Basically the development of numerous uncontrolled cells in the tissues create abnormality which later on leads to tumor in lungs. It is necessary to detect lung cancer in earlier stages, if left untreated its growth may spread into other nearby parts of body. For diagnosis, Patients may undergo several imaging tests such as CT scan, Chest X-ray and PET scan.In the existing recognition and detection techniques the Micro vessel density (MVD) analysis is used from which geometrical features are extracted to detect the tumorin lungs. In alternate to this the Gray level co-occurrence matrix (GLCM) may also be used with the geometrical features of the image to obtain more accurate result of lung cancer detection. GLCM features such as image contrast, homogeneity, dissimilarity, energy and correlation is beneficial to obtain results with higher accuracy. On the basis of significant instrument, novel lung cancer prediction framework will be developed.
A population-based cohort study of chest x-ray screening in smokers: lung cancer detection findings and follow-up