Classifying pulmonary nodules using dynamic enhanced single slice and multi slice CT images

Pulmonary nodules are classified into three types such as solid, mixed GGO, and pure GGO types on the basis of the visual assessment of CT appearance. In our current study a quantitative classification algorithm has been developed by using volumetric data sets obtained from thin-section CT images. The algorithm can classify the pulmonary nodules into five types (alpha, beta, gamma, delta, and epsilon) on the basis of internal features extracted from CT number histograms inside nodules. We applied dynamic enhanced single slice and multi slice CT images to this classification algorithm and we analyzed it in each type.