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Accurate segmentation of small pulmonary nodules (SPNs) on thoracic CT images is an important technique for volumetric doubling time estimation and feature characterization for the diagnosis of SPNs. Most of the nodule segmentation algorithms that have been previously presented were designed to handle solid pulmonary nodules. However, SPNs with ground-glass opacity (GGO) also affects a diagnosis. Therefore, we have developed an automated volumetric segmentation algorithm of SPNs with GGO on thoracic CT images. This paper presents our segmentation algorithm with multiple fixed-thresholds, template-matching method, a distance-transformation method, and a watershed method. For quantitative evaluation of the performance of our algorithm, we used the first dataset provided by NCI Lung Image Database Consortium (LIDC). In the evaluation, we employed the coincident rate which was calculated with both the computerized segmented region of a SPN and the matching probability map (pmap) images provided by LIDC. As the result of 23 cases, the mean of the total coincident rate was 0.507 +/- 0.219. From these results, we concluded that our algorithm is useful for extracting SPNs with GGO and solid pattern as well as wide variety of SPNs in size.
Tachibana et al. (Thu,) studied this question.