Automatic Landmark Detection and Non-linear Landmark- and Surface-based Registration of Lung CT Images

Registration of the lungs in thoracic CT images is required in many fields of application in medical imaging, for example for motion estimation or analysis of pathology progression. In this paper, we present a feature-based registration approach for lung CT images based on lung surfaces and automatically detected inner-lung landmark pairs. In a first step, an affine pre-registration of surface models generated from lung segmentation masks is performed. Following, an automatic algorithm is used for the landmark identification and landmark transfer between fixed and moving image. The result of this landmark detection and the result of a non-linear diffusion-based surface registration are used to generate the final deformation field by thin-plate-splines interpolation. The approach is evaluated based on 20 CT scans provided for the EMPIRE10 study for pulmonary image registration. In this study, the approach reached a final placement of 21 out of 34 participating algorithms. The evaluation shows a very good alignment of lung boundaries in contrast to a disappointing matching of inner lung structures, although landmark pairs were detected correctly with the automatic algorithm.