Spatio-temporal modeling of lung images for cancer detection

Li Shen, Wei Zheng, Ling Gao, Heng Huang, Fillia Makedon, Justin Pearlman

Research output: Contribution to journalArticle

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1085-1089
Number of pages5
JournalOncology reports
Volume15
Issue number4
StatePublished - Apr 1 2006
Externally publishedYes

Keywords

  • Perfusion magnetic resonance imaging
  • Pulmonary nodule
  • Registration
  • Segmentation
  • Time-intensity profile

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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  • Cite this

    Shen, L., Zheng, W., Gao, L., Huang, H., Makedon, F., & Pearlman, J. (2006). Spatio-temporal modeling of lung images for cancer detection. Oncology reports, 15(4), 1085-1089.