Computer-aided classification of pulmonary nodules in surrounding and internal feature spaces using three-dimensional thoracic CT images

Abstract

The detection rate of small pulmonary lesions has recently increased due to the advances in imaging technology such as Multi-slice CT scanner. In assessing the malignant potential of small pulmonary nodules in thin-section CT images, it is important to examine the nodule internal structure. In our previous work, we found that internal structure features derived from CT density and curvature indexes such as shape index and curvedness were useful for differentiating malignant and benign nodules in 3-D thoracic CT images. This may be attributed to the texture changes in the nodule region due to a developing malignancy. The relationship between nodules and their surrounding structures such as vessel, bronchi, and pleura are another important cue to classification between malignant and benign nodules. We therefore develop a scheme to analyze surrounding structures of the nodule using differential geometry based vector fields in 3-D thoracic images. In addition we present a joint histogram-based representation approach of the internal and surrounding structures of the nodule to visualize the characteristics between nodules. In the present study, we explore the feasibility of combining internal and surrounding structure features for classification of pulmonary nodules.