A Novel Approach for Automatic Follow-Up of Detected Lung Nodules
Our long term research goal is to develop an image-based approach for early diagnosis of lung nodules that may lead to lung cancer. This paper focuses on monitoring the progress of detected lung nodules in successive chest low dose CT (LDCT) scans of a patient using non-rigid registration. In this paper, we propose a new methodology for 3D LDCT data registration. The registration methodology is non-rigid and involves two steps: global alignment of one scan (target data) to another scan (reference data) using the learned prior appearance model followed by local alignments in order to correct for intricate deformations. From two subsequent chest scans, visual appearance of the chest images, after equalizing their signals, are modeled with a Markov-Gibbs random field with pairwise interaction. Our approach is based on finding the affine transformation to register one data set (target data) to another data set (reference data) by maximizing a special Gibbs energy function using a gradient descent algorithm. To get accurate appearance model, we developed a new approach to an automatically select the most important cliques that describe the visual appearance of LDCT data. To handle local deformations, we propose a new approach based on deforming each voxel over evolving closed and equi-spaced surfaces (iso-surfaces) to closely match the prototype. The evolution of the iso-surfaces is guided by an exponential speed function in the directions minimizing distances between corresponding pixel pairs on the iso-surfaces on both data sets. Our preliminary results on 10 patients show that the proper registration could lead to precise identification of the progress of the detected lung nodules.