Yeny Yim

Learn More
We propose a new curvature-based method for correcting the segmented lung boundary. Our method consists of the following steps. First, the lungs are extracted from chest CT images by the automatic segmentation method. Second, the segmented lung contours are corrected by lung smoothing in each axial slice. Our scan line search provides an efficient contour(More)
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(More)
We propose a novel non-rigid registration method that computes the correspondences of two deformable surfaces using the cover tree. The aim is to find the correct correspondences without landmark selection and to reduce the computational complexity. The source surface S is initially aligned to the target surface T to generate a cover tree from the densely(More)
We propose a new method for correcting the segmented lung boundary in expiratory and inspiratory CT. First, the initial lung boundary is extracted by using density-based segmentation. Second, the scope for the boundary propagation is computed by generating and analyzing the gradient profiles with an adaptive length. The definition of the scope helps to(More)
Surgeons use information from multiple sources when making surgical decisions. These include volumetric datasets (such as CT, PET, MRI, and their variants), 2D datasets (such as endoscopic videos), and vector-valued datasets (such as computer simulations). Presenting all the information to the user in an effective manner is a challenging problem. In this(More)
Registration of preoperative CT data to intra-operative video images is necessary not only to compare the outcome of the vocal fold after surgery with the preplanned shape but also to provide the image guidance for fusion of all imaging modalities. We propose a 2D-3D registration method using gradient-based mutual information. The 3D CT scan is aligned to(More)