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Proceedings Paper

Spatio-temporal analysis tool for modeling pulmonary nodules in MR images
Author(s): Li Shen; Heng Huang; James Ford; Chia-Hsin Lu; Ling Gao; Wei Zheng; Fillia Makedon; Justin Pearlman
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Paper Abstract

To detect lung cancer at an earlier stage, a promising method is to apply perfusion magnetic resonance imaging (pMRI) modified to assess tumor angiogenesis. One key issue is to effectively characterize angiogenic patterns of pulmonary nodules. Based on our previous study addressing this issue, in this work, we develop STAT, a Spatio-Temporal Analysis Tool that implements not only our previously proposed pulmonary nodule modeling framework but also a user friendly interface and many extended functions. Our goal is to make STAT an easy-to-use tool that can be applied to more general cases. STAT employs the following overall strategy for modeling pulmonary nodules: (1) nodule identification using a correlation maximization method, (2) nodule segmentation using edge detection, morphological operations and model-based strategy, and (3) nodule registration using landmark approach and thin-plate spline interpolation. In nodule identification, STAT provides new schemes for selecting the template and refining results in difficult cases. In nodule segmentation, STAT provides additional flexibilities for creating the weighting mask, selecting morphological structure elements and individually fixing segmentation result. In nodule registration, our previous study uses principal component analysis for landmark extraction, which may not work in general. To overcome this limitation, STAT provides an enhanced approach that minimizes the bending energy of the thin plate spline interpolation or mean square distance between each landmark set and the template set. Our main application of STAT is to define blood arrival patterns in the lung to identify tumor angiogenesis as a means of early accurate diagnosis of cancer.

Paper Details

Date Published: 10 March 2006
PDF: 10 pages
Proc. SPIE 6141, Medical Imaging 2006: Visualization, Image-Guided Procedures, and Display, 61412I (10 March 2006); doi: 10.1117/12.654469
Show Author Affiliations
Li Shen, Univ. of Massachusetts Dartmouth (United States)
Heng Huang, Dartmouth College (United States)
James Ford, Dartmouth College (United States)
Chia-Hsin Lu, Univ. of Massachusetts Dartmouth (United States)
Ling Gao, Dartmouth Medical School (United States)
Wei Zheng, Dartmouth College (United States)
Fillia Makedon, Dartmouth College (United States)
Justin Pearlman, Dartmouth Medical School (United States)

Published in SPIE Proceedings Vol. 6141:
Medical Imaging 2006: Visualization, Image-Guided Procedures, and Display
Kevin R. Cleary; Robert L. Galloway, Jr., Editor(s)

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