Using non-rigid image registration and Thin-Plate Spline warping for lung cancer progression assessment

Among different types of cancer the lung cancer is the most aggressive and best practice to its accurate prognosis is the determination of the current stage of the disease. Three main factors in cancer staging are primary tumor, regional lymph nodes and metastasis. Accurate determination of the cancer stage is not a trivial task, however utilization of computerized diagnostic tools can help the cancer experts to better diagnose the cancer status. Image registration is one of the main tools used for cancer diagnosis by experts. In this paper a new approach based on image registration is proposed to help the experts in better diagnosis of the lung cancer which is an improved method based on a previously proposed approach by the authors. In a multistep process the similarity between a pair of images acquired during past and current cancer stages is maximized with regards to the fact that tumor changes should be preserved during the deformation process. The similarity measure used during the registration process is Normalized Mutual Information. In the non-rigid image registration phase constraints are enforced on the optimization criteria, number of iterations and number of B-Spline grid nodes to preserve the tumor change. Control points which are transformation parameters for the Thin-Plate Spline warping are extracted from edge detected images in a semiautomatic manner. In the final step subtraction of sequential CT images is performed to detect the changes in the lung including the tumor change. Using the Insight Toolkit framework improved the quality of the final results and also ensured a more robust application. Experiments conducted on 8 pairs of CT lung volumes proved to have a satisfactory quality for change detection of the lung cancer.