Automated volumetric segmentation method for computerized-diagnosis of pure nodular ground-glass opacity in high-resolution CT

While accurate diagnosis of pure nodular ground glass opacity (PNGGO) is important in order to reduce the number of unnecessary biopsies, computer-aided diagnosis of PNGGO is less studied than other types of pulmonary nodules (e.g., solid-type nodule). Difficulty in segmentation of GGO nodules is one of technical bottleneck in the development of CAD of GGO nodules. In this study, we propose an automated volumetric segmentation method for PNGGO using a modeling of ROI histogram with a Gaussian mixture. Our proposed method segments lungs and applies noise-filtering in the pre-processing step. And then, histogram of selected ROI is modeled as a mixture of two Gaussians representing lung parenchyma and GGO tissues. The GGO nodule is then segmented by region-growing technique that employs the histogram model as a probability density function of each pixel belonging to GGO nodule, followed by the elimination of vessel-like structure around the nodules using morphological image operations. Our results using a database of 26 cases indicate that the automated segmentation method have a promising potential