Abstract-Defining the support (or frequency) of a subgraph is trivial when a database of graphs is given: it is simply the number of graphs in the database that contain the subgraph. However, if the input is one large graph, an appropriate support definition is much more difficult to find. In this paper we study the core problem, namely overlapping embeddings of the subgraph, in detail and suggest a definition that relies on the non-existence of equivalent ancestor embeddings in order to guarantee that the resulting support is anti-monotone. We prove this property and describe a method to compute the support defined in this way.

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With the aim of reducing the radiologists' subjectivity and the high degree of inter-observer variability, Contentbased Image Retrieval (CBIR) systems have been proposed to provide visual comparisons of a given lesion to a collection of similar lesions of known pathology. In this paper, we present the effectiveness of shape features versus texture features for calculating lung nodules' similarity in Computed Tomography (CT) studies. In our study, we used eighty-five cases of thoracic CT data from the Lung Image Database Consortium (LIDC). To encode the shape information, we used the eight most commonly used shape features for pulmonary nodule detection and diagnosis by existent CAD systems. For the texture, we used co-occurrence, Gabor, and Markov features implemented in our previous CBIR work. Our preliminary results give low overall precision results for shape compared to texture, showing that shape features are not effective by themselves at capturing all the information we need to compare the lung nodules.