Application of Intelligent Computational Models on Computed Tomography Lung Images

With computed tomography (CT) scanners, hundreds of slices are generated to visualize the condition of lung per patient. The analysis on slices-by-slices dataset is time-consuming for radiologists. Therefore, automated identification of abnormalities on CT lung images is vital to assist the radiologists to make an interpretation and decision. In this paper, we review the performance of various conventional and computational intelligence algorithms in the segmentation, detection and quantification of lung nodules on CT lung images. The accuracy of lung region segmentation is found important as a preprocessing step to identify the lung nodules. By mean of these computerized systems, the detection and measurement of lung nodules can assist the radiologists to determine whether the lung nodules are benign or malignant.