Scene Analysis with Structural Prototypes for Content-Based Image Retrieval in Medicine

摘要

The content of medical images can often be described as a composition of relevant objects with distinct relationships. Each single object can then be represented as a graph node, and local features of the objects are associated as node attributes, e.g. the centroid coordinates. The relations between these objects are represented as graph edges with annotated relational features, e.g. their relative size. Nodes and edges build an attributed relational graph (ARG). For a given setting, e.g. a hand radiograph, a generalization of the relevant objects, e.g. individual bone segments, can be obtained by the statistical distributions of all attributes computed from training images. These yield a structural prototype graph consisting of one attributed node per relevant object and of their relations represented as attributed edges. In contrast to the ARG, the mean and standard deviation of each local or relational feature are used to annotate the prototype nodes or edges, respectively. The prototype graph can then be used to identify the generalized objects in new images. As new image content is represented by hierarchical attributed region adjacency graphs (HARAGs) which are obtained by region-growing, the task of object or scene identification corresponds to the problem of inexact sub-graph matching between a small prototype and the current HARAG. For this purpose, five approaches are evaluated in an example application of bone-identification in 96 radiographs: Nested Earth Mover's Distance, Graph Edit Distance, a Hopfield Neural Network, Pott's Mean Field Annealing and Similarity Flooding. The discriminative power of 34 local and 12 relational features is judged for each object by sequential forward selection. The structural prototypes improve recall by up to 17% in comparison to the approach without relational information.