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Proceedings Paper

Novel level-set based segmentation method of the lung at HRCT images of diffuse interstitial lung disease (DILD)
Author(s): Jeongjin Lee; Joon Beom Seo; Namkug Kim; Sang Ok Park; Ho Lee; Yeong Gil Shin; Soo-Hong Kim
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Paper Abstract

In this paper, we propose an algorithm for reliable segmentation of the lung at HRCT of DILD. Our method consists of four main steps. First, the airway and colon are segmented and excluded by thresholding(-974 HU) and connected component analysis. Second, initial lung is identified by thresholding(-474 HU). Third, shape propagation outward the lung is performed on the initial lung. Actual lung boundaries exist inside the propagated boundaries. Finally, subsequent shape modeling level-set inward the lung from the propagated boundary can identify the lung boundary when the curvature term was highly weighted. To assess the accuracy of the proposed algorithm, the segmentation results of 54 patients are compared with those of manual segmentation done by an expert radiologist. The value of 1 minus volumetric overlap is less than 5% error. Accurate result of our method would be useful in determining the lung parenchyma at HRCT, which is the essential step for the automatic classification and quantification of diffuse interstitial lung disease.

Paper Details

Date Published: 27 March 2009
PDF: 8 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725941 (27 March 2009); doi: 10.1117/12.811327
Show Author Affiliations
Jeongjin Lee, Catholic Univ. of Korea (Korea, Republic of)
Joon Beom Seo, Univ. of Ulsan College of Medicine (Korea, Republic of)
Namkug Kim, Univ. of Ulsan College of Medicine (Korea, Republic of)
Sang Ok Park, Univ. of Ulsan College of Medicine (Korea, Republic of)
Ho Lee, Seoul National Univ. (Korea, Republic of)
Yeong Gil Shin, Seoul National Univ. (Korea, Republic of)
Soo-Hong Kim, Sangmyung Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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