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.
