A minimal path searching approach for active shape model (ASM)-based segmentation of the lung

Shengwen Guo; Baowei Fei

doi:10.1117/12.812575

27 March 2009 A minimal path searching approach for active shape model (ASM)-based segmentation of the lung

Shengwen Guo, Baowei Fei

Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72594B (2009) https://doi.org/10.1117/12.812575
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

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.

Citation Download Citation

Shengwen Guo and Baowei Fei "A minimal path searching approach for active shape model (ASM)-based segmentation of the lung", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72594B (27 March 2009); https://doi.org/10.1117/12.812575

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