Automatic segmentation and registration of lung surfaces in temporal chest CT scans

Authors:
Helen Hong;Jeongjin Lee;Yeni Yim;Yeong Gil Shin
Affiliations:
School of Computer Science and Engineering, BK21: Information Technology, Seoul National University, Seoul, Korea;School of Computer Science and Engineering, Seoul National University, Seoul, Korea;School of Computer Science and Engineering, Seoul National University, Seoul, Korea;School of Computer Science and Engineering, Seoul National University, Seoul, Korea
Venue:
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Year:
2005

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Smoothing segmented lung boundary in chest CT images using scan line search

CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications

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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.