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Proceedings Paper

Sparse appearance model-based algorithm for automatic segmentation and identification of articulated hand bones
Author(s): Fitsum A. Reda; Zhigang Peng; Shu Liao; Yoshihisa Shinagawa; Yiqiang Zhan; Gerardo Hermosillo; Xiang Sean Zhou
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Paper Abstract

Automatic and precise segmentation of hand bones is important for many medical imaging applications. Although several previous studies address bone segmentation, automatically segmenting articulated hand bones remains a challenging task. The highly articulated nature of hand bones limits the effectiveness of atlas-based segmentation methods. The use of low-level information derived from the image-of-interest alone is insufficient for detecting bones and distinguishing boundaries of different bones that are in close proximity to each other. In this study, we propose a method that combines an articulated statistical shape model and a local exemplar-based appearance model for automatically segmenting hand bones in CT. Our approach is to perform a hierarchical articulated shape deformation that is driven by a set of local exemplar-based appearance models. Specifically, for each point in the shape model, the local appearance model is described by a set of profiles of low-level image features along the normal of the shape. During segmentation, each point in the shape model is deformed to a new point whose image features are closest to the appearance model. The shape model is also constrained by an articulation model described by a set of pre-determined landmarks on the finger joints. In this way, the deformation is robust to sporadic false bony edges and is able to fit fingers with large articulations. We validated our method on 23 CT scans and we have a segmentation success rate of ~89.70 %. This result indicates that our method is viable for automatic segmentation of articulated hand bones in conventional CT.

Paper Details

Date Published: 21 March 2014
PDF: 7 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90341O (21 March 2014); doi: 10.1117/12.2043262
Show Author Affiliations
Fitsum A. Reda, Siemens Medical Solutions USA, Inc. (United States)
Vanderbilt Univ. (United States)
Zhigang Peng, Siemens Medical Solutions USA, Inc. (United States)
Shu Liao, Siemens Medical Solutions USA, Inc. (United States)
Yoshihisa Shinagawa, Siemens Medical Solutions USA, Inc. (United States)
Yiqiang Zhan, Siemens Medical Solutions USA, Inc. (United States)
Gerardo Hermosillo, Siemens Medical Solutions USA, Inc. (United States)
Xiang Sean Zhou, Siemens Medical Solutions USA, Inc. (United States)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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