Segmentation of cancer nodules in lung using radial basis function network and fuzzy C mean clustering

Abstract

Cancer in the lung is a vital cause of major death for women and men in oncology. Initial detection of the cancer is supportive of a remedy to cure the disease completely. The mechanism of image processing remains widely improving in numerous medical areas for initial identification and therapy. The timeline is significant in diagnosing the patient whether they have cancer disease. The requirement to notice the presence of cancer nodules in the initial stage is growing. The original contributions of this work are the use of Chest Computed Tomography (CT) image on a diagnosis of cancer nodules in the lung. The available lung cancer images are passing through stages to attain quality and accurateness in experimental results. Initially, different image processing algorithms are used in removing the lung region alone from chest CT images. Fuzzy C-Mean and Radial-basis performance network algorithm is used as the stages in the image segmentation technique. Fuzzy C Means algorithm uses the segment of the lungs. Using these segmentation methods, cancers are segmented separately with no loss of information.