title: Modeling Time-Intensity Profiles for Pulmonary Nodules in MR Images.
abstract: Perfusion magnetic resonance imaging (pMRI) is an important tool to assess tumor angiogenesis for the early detection of lung cancer. This paper 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 nodule boundary, and 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. Time intensity profiles of nodules region capture important angiogenic patterns in the lung that can distinguish between cancer and benign nodules and help early detection.
authors.affiliation: Department of Computer and Information Science, University of Massachusetts Dartmouth, 285 Old Westport Rd, N. Dartmouth, MA 02747, SA (Phone 508 910 6691, Fax 508 999 9144, 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