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.
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