Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Digital Image Processing
CAD System for the Assistance of Comparative Reading for Lung Cancer Using Serial Helical CT Images
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Automatic 3D Registration of Lung Surfaces in Computed Tomography Scans
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Automatic 3D Registration of Lung Surfaces in Computed Tomography Scans
Automatic 3D Registration of Lung Surfaces in Computed Tomography Scans
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Automatic retinal image registration scheme using global optimization techniques
IEEE Transactions on Information Technology in Biomedicine
Computers in Biology and Medicine
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We propose an automatic segmentation and registration method that provides more efficient and robust matching of lung nodules in sequential chest computed tomography (CT) images. Our method consists of four steps. First, the lungs are extracted from chest CT images by the automatic segmentation method. Second, gross translational mismatch is corrected by optimal cube registration. This initial alignment does not require extracting any anatomical landmarks. Third, the initial alignment is step-by-step refined by hierarchical surface registration. To evaluate the distance measures between lung boundary points, a three-dimensional distance map is generated by narrow-band distance propagation, which drives fast and robust convergence to the optimal value. Finally, correspondences of manually detected nodules are established from the pairs with the smallest Euclidean distances. Experimental results show that our segmentation method accurately extracts lung boundaries and the registration method effectively finds the nodule correspondences.