Multimodal image registration of lung images

Lung tumor estimation on imaging modalities is required to assess the extent of the tumor for diagnosis. Segmentation of tumor in Cone-Beam Computed Tomography (CBCT) images is non-trivial due to its imaging artifacts. Here we propose a novel technique for image registration of 18-Fluoro deoxyglucose Positron Emission Tomography (PET) and Computed Tomography(CT) images with CBCT images. The computation is performed in two stages. In the first stage, mutual information based rigid image registration is performed to obtain a rough global alignment of CBCT image with the corresponding PET and CT images. This result is fed to the second stage to perform deformable image registration between a pair of corresponding CBCT volumes of the same patient captures at different time instances using a viscous fluid model. The technique is adapted in both 2D (for slicewise computation) and 3D space (for computing with volume), and a comparative performance is presented with a simulated deformation model.