Spatio-temporal modeling of lung images for cancer detection.
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.
Shen
Li
L
Department of Computer and Information Science, University of Massachusetts-Dartmouth, North Dartmouth, MA 02747, USA. lshen@umassd.edu
Zheng
Wei
W
Gao
Ling
L
Huang
Heng
H
Makedon
Fillia
F
Pearlman
Justin
J
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