Automatic Segmentation and Registration of Lung Surfaces in Temporal Chest CT Scans

Abstract

We propose an automatic segmentation and registration method for matching lung surfaces of temporal CT scans. Our method consists of three steps. First, an automatic segmentation is used for accurately identifying lung surfaces. Second, initial registration using an optimal cube is performed for correcting the gross translational mismatch. Third, the initial alignment is step by step refined by the iterative surface registration. For the fast and robust convergence of the distance measure to the optimal value, a 3D distance map is generated by the narrow band distance propagation. Experimental results show that our segmentation and registration method extracts accurate lung surfaces and aligns them much faster than conventional ones using a distance measure.