title: Automatic lung nodule matching on sequential CT images.
abstract: 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.
authors.affiliation: Division of Multimedia Engineering, College of Information and Media, Seoul Women's University, 126 Gongreung-Dong, Nowon-Gu, Seoul 139-774, Korea. hlhong@swu.ac.kr <hlhong@swu.ac.kr>
authors.forename: Helen
authors.initials: H
authors.lastname: Hong
authors.forename: Jeongjin
authors.initials: J
authors.lastname: Lee
authors.forename: Yeny
authors.initials: Y
authors.lastname: Yim
mesh_terms: D000465:Algorithms; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed