Lung Uniformization for Juxta-Pleural Nodule Detection

We propose a method for automatic lung juxtapleural nodule detection in thorax CT images, to be used as a Computer Assisted Detection (CAD) tool by radiologists. It is based on the calculation and automatic analysis of local curvature on the lung surface as extracted from high-resolution CT scans, and exploits uniformization to a sphere (e.g. through conformal mapping) to allow a global view of the lung surface, with marking of high curvature regions which can be suspected of being pleural nodules. Schematically, the tool works as follows. First, lung binary masks are extracted from the image by 3D segmentation of the CT scan. On these masks, pleural nodules appear as small surface concavities of the mask surface. After patching the entrance of vessels into the parenchyma in the hilus pulmonis, the lung frontier 'Sigma' is a smooth genus-0 surface. This surface is triangulated and is then uniformized to a sphere 'Sigma-prime' . In this parameterization a suitable function Psi of the mean and the Gaussian curvatures can be calculated over Sigma. Function Psi is displayed as a colour variation onto both Sigma and Sigma-prime, so marking regions that represent high-curvature concavities. A threshold on Psi is then applied and regions of interest (ROIs), containing little concavities with a low radius of curvature (such as pleural nodules), are detected. ROIs are then examined and classified; techniques such as spherical wavelets are available on the sphere, which will be used to distinguish between false and true positives, helping in diagnosing pleural nodules.