Three-dimensional multicriterion automatic segmentation of pulmonary nodules of helical computed tomography images

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Abstract
A 3-D multicriterion automatic segmentation algorithm is developed to improve accuracy of delineation of pulmonary nodules on helical computed tomography (CT) images by removing their adjacent structures. The algorithm applies multiple gray-value thresholds to a nodule region of interest (ROi). At each threshold level, the nodule candidate in the ROi is automatically detected by labeling 3-D connected components, followed by a 3-D morphologic opening operation. Once the nodule candidate is found, its two specific parameters, gradient strength of the nodule surface and a 3-D shape compactness factor, can be computed. The optimal threshold can be determined by analyzing these parameters. Our experiments with in vivo nodules demonstrate the feasibility of employing this algorithm to improve the accuracy of nodule delineation, especially for small nodules less than 1 cm in diameter. This discloses the potential of the algorithm for accurate characterizations of nodules (e.g., volume, change in volume over time) at an early stage, which can help to provide valuable guidance for further clinical management.

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