Statistical shape representation with landmark clustering by solving the assignment problem

Bulat Ibragimov; Boštjan Likar; Franjo Pernuš; Tomaž Vrtovec

doi:10.1117/12.2006176

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13 March 2013 Statistical shape representation with landmark clustering by solving the assignment problem

Bulat Ibragimov, Boštjan Likar, Franjo Pernuš, Tomaž Vrtovec

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Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86690E (2013) https://doi.org/10.1117/12.2006176
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Statistical shape modeling is considered as a backbone of image analysis, since shapes capture distinguishable geometrical properties of depicted objects and spatial relationships among the objects. In the field of medical image analysis, a shape allows segmentation and registration of complex and/or poorly visible structures, where geometrical information may have a more crucial role than pure intensity information. In this paper, we present a novel statistical shape model based on landmark positions and spatial relationships among landmarks. A given training set of images is first annotated by a set of landmarks, which represents the shape of the object of interest. In contrast to active shape (ASM) and appearance models (AAM), where a shape is a single object characterized by a system of eigenvectors, we describe a shape as a combination of distances and angles between landmarks. Finding a suitable combination of distances and angles is achieved by optimizing the representativeness of the model (i.e. the distances and angles must describe the shape and its plasticity), and complexity of the model (i.e. the number of distances and angles must be acceptable for practical applications). To generate a model that satisfies these conditions, the landmarks are first separated into clusters, which are then optimally connected. The optimal connections between clusters are generated by using the assignment problem. The obtained model combined with the game-theoretic framework was applied to segment lung fields from chest radiographs. The usage of such simplified model results on average in a 0.05 mm deterioration of the segmentation performance in terms of the symmetric mean boundary distance, and in a 3.3-times acceleration of the computational time.

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Bulat Ibragimov, Boštjan Likar, Franjo Pernuš, and Tomaž Vrtovec "Statistical shape representation with landmark clustering by solving the assignment problem", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86690E (13 March 2013); https://doi.org/10.1117/12.2006176

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