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We propose a robust and accurate algorithm for segmenting the 3D pulmonary nodules in multislice CT scans. The solution unifies i) the parametric Gaussian model fitting of the volumetric data evaluated in Gaussian scale-space and ii) non-parametric 3D segmentation based on normalized gradient (mean shift) ascent defining the basin of attraction of the target tumor in the 4D spatial-intensity joint space. This realizes the 3D segmentation according to both spatial and intensity proximities simultaneously. Experimental results show that the system reliably segments a variety of nodules including part- or non-solid nodules which poses difficulty for the existing solutions. The system also processes a 32x32x32-voxel volume-of-interest efficiently by six seconds on average.

Robust 3D Segmentation of Pulmonary Nodules in Multislice CT Images