A computerized approach for estimating pulmonary nodule growth rates in three-dimensional thoracic CT images based on CT density histogram

In research and development of computer-aided differential diagnosis, there is now a widespread interest in the use of nodule doubling time for measuring the volumetric changes of pulmonary nodule. To assess nodule status requires not only the measurement of volume changes but also one of nodule density variations. This paper proposes 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 classification based on CT density histogram and size, (4) computation of doubling time based on CT density histogram, and (5) classification between benign and malignant. Our approach was applied to follow-up scans of lung nodules. The preliminary experimental result demonstrated that our approach has a highly potential usefulness to assess the nodule evolution using high-resolution CT images.

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