title: Spatio-temporal modeling of lung images for cancer detection.
abstract: Perfusion magnetic resonance imaging (pMRI) is an important tool in assessing tumor angiogenesis for the early detection of lung cancer. This study presents a novel integrated framework for spatio-temporal modeling of pulmonary nodules in pMRI image sequences. After localizing a nodule region in each image, we perform segmentation in the region to extract the nodule boundary, then use thin-plate spline interpolation for nodule registration along the temporal dimension. The resulting spatio-temporal model can lead to many types of nodule characterization, e.g. a time-intensity profile of a nodule region, and be used to capture important angiogenic patterns in the lung that can distinguish between cancer and benign nodules and assist in early detection.
authors.affiliation: Department of Computer and Information Science, University of Massachusetts-Dartmouth, North Dartmouth, MA 02747, USA. lshen@umassd.edu
authors.forename: Li
authors.initials: L
authors.lastname: Shen
authors.forename: Wei
authors.initials: W
authors.lastname: Zheng
authors.forename: Ling
authors.initials: L
authors.lastname: Gao
authors.forename: Heng
authors.initials: H
authors.lastname: Huang
authors.forename: Fillia
authors.initials: F
authors.lastname: Makedon
authors.forename: Justin
authors.initials: J
authors.lastname: Pearlman
mesh_terms: D003937:Diagnosis, Differential; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008171:Lung Diseases / Q000175:diagnosis / Q000473:pathology; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D018810:Magnetic Resonance Angiography / Q000706:statistics & numerical data*; D008962:Models, Theoretical