Figure 1. Application of surface normal overlap (SNO)-CAD and lantern transform to nodule detection in human lung. A, Detail of transverse CT section shows pulmonary nodule in posterior portion of right upper lobe. Applied threshold indicates isosurface (red) between air and tissue. B, Schematic representation of surface normal vectors (blue) generated at isosurface (red) in same lung region as that bounded by aqua box in A. Normals are generated in three dimensions in volumetric CT data at all isosurfaces in the thick region extracted during segmentation. C, Map of SNO-CAD scores for all voxels displayed in A, calculated by using a clustering algorithm to quantify convergence of normal vectors, confirms identification of pulmonary nodule. Scale at bottom indicates 12-bit scaling of SNO-CAD scores (0-8). D, Image with superimposed schema shows application of lantern transform to nodule in contact with blood vessel. Rays of visibility (black radii) cast from a SNO-CAD-identified nodule candidate are used to generate an approximately spherical surface (red). E, Image with superimposed schema shows application of lantern transform to SNO-CAD-identified nodule candidate in pulmonary vessel. Rays of visibility generate an ellipsoid surface less spherical than that in D. Quantitative characteristics of this ellipsoid result in its rejection as a nodule candidate, while those of the ellipsoid in D allow it to remain a nodule candidate.