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Proceedings Paper

A minimal path searching approach for active shape model (ASM)-based segmentation of the lung
Author(s): Shengwen Guo; Baowei Fei
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Paper Abstract

We are developing a minimal path searching method for active shape model (ASM)-based segmentation for detection of lung boundaries on digital radiographs. With the conventional ASM method, the position and shape parameters of the model points are iteratively refined and the target points are updated by the least Mahalanobis distance criterion. We propose an improved searching strategy that extends the searching points in a fan-shape region instead of along the normal direction. A minimal path (MP) deformable model is applied to drive the searching procedure. A statistical shape prior model is incorporated into the segmentation. In order to keep the smoothness of the shape, a smooth constraint is employed to the deformable model. To quantitatively assess the ASM-MP segmentation, we compare the automatic segmentation with manual segmentation for 72 lung digitized radiographs. The distance error between the ASM-MP and manual segmentation is 1.75 ± 0.33 pixels, while the error is 1.99 ± 0.45 pixels for the ASM. Our results demonstrate that our ASM-MP method can accurately segment the lung on digital radiographs.

Paper Details

Date Published: 27 March 2009
PDF: 8 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72594B (27 March 2009); doi: 10.1117/12.812575
Show Author Affiliations
Shengwen Guo, Emory Univ. (United States)
Baowei Fei, Emory Univ. (United States)


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

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