Growth-rate estimation of pulmonary nodules in three-dimensional thoracic CT images based on CT density histogram analysis and its application to nodule classification

Abstract

In research and development of computer-aided differential diagnosis using thoracic CT images, there is now widespread interest in the use of nodule doubling time for measuring the volumetric changes of pulmonary nodule. The evolution pattern of each nodule might depend on the CT density distribution pattern inside nodule such as pure GGO, mixed GGO, or solid nodules. This paper presents a computerized approach to measure nodule density variation inside small pulmonary nodule using CT images. The approach consists of five steps: (1) nodule segmentation, (2) computation of CT density histogram, (3) nodule categorization (α, β, γ, δ, and e) based on CT density histogram, (4) computation of doubling time based on CT density histogram, and (5) classification between benign and malignant cases. Using our dataset of follow-up scans of pulmonary nodules, we evaluated evaluation patterns of nodules on the basis of the predominant five nodule categorizations and designed the classification approach between benign and malignant cases. The preliminary experimental result demonstrated that our approach has a potential usefulness to assess the nodule evolution using thoracic CT images.