Lung nodule segmentation in computed tomography (CT) plays an important role in computer-aided detection, diagnosis,
and quantification systems for lung cancer. In this study, we developed a simple but accurate nodule segmentation
method in three-dimensional (3D) CT. First, a volume of interest (VOI) was determined at the location of a nodule. We
then transformed the VOI into a two-dimensional (2D) image by use of a "spiral-scanning" technique, in which a radial
line originating from the center of the VOI spirally scanned the VOI. The voxels scanned by the radial line were
arranged sequentially to form a transformed 2D image. Because the surface of a nodule in 3D image became a curve in
the transformed 2D image, the spiral-scanning technique considerably simplified our segmentation method and enabled
us to obtain accurate segmentation results. We employed a dynamic programming technique to delineate the "optimal"
outline of a nodule in the 2D image, which was transformed back into the 3D image space to provide the interior of the
nodule. The proposed segmentation method was trained on the first and was tested on the second Lung Image Database
Consortium (LIDC) datasets. An overlap between nodule regions provided by computer and by the radiologists was
employed as a performance metric. The experimental results on the LIDC database demonstrated that our segmentation
method provided relatively robust and accurate segmentation results with mean overlap values of 66% and 64% for the
nodules in the first and second LIDC datasets, respectively, and would be useful for the quantification, detection, and
diagnosis of lung cancer.