Computed Tomography (CT) is generally accepted as the most sensitive way for lung cancer screening. Its high contrast resolution allows the detection of small nodules and, thus, lung cancer at a very early stage. Due to the amount of data it produces, however, automating the nodule detection process is viable. The challenging problem for any nodule detection system is to keep low false-positive detection rate while maintaining high sensitivity. In this paper, we first describe a 3D filter-based method for pulmonary micronodule detection from high-resolution 3D chest CT images. Then, we propose a false-positive elimination method based on a deformable model. Finally, we present promising results of applying our method to various clinical chest CT datasets with over 90% detection rate. The proposed method focuses on the automatic detection of both calcified (high-contrast) and noncalcified (low-contrast) granulomatous nodules less than 5mm in diameter.
A vascular network is often represented by a Reeb graph, which is a topological skeleton, and graph theory has been widely applied to analyze properties of a vascular network. A Reeb graph model for a vascular network is obtained by assigning the branch points of the network to be the vertices of the graph and the vessels between branch points to be the edges of the graph. Vascular networks develop by way of angiogenesis, a growth process that involves the biological mechanisms of vessel sprouting (budding) and splitting (intussusception). Vascular networks develop by way of two biological mechanisms of vessel sprouting (budding) and splitting (intussusception). According to a graph theory modeling of two vascular network growth mechanisms, all nodes in the Reeb graph must be cubic in degree except for two special nodes: the afferent (A) and efferent (E) nodes. We define that a vascular network is cubic if all internal nodes are cubic in degree. We consider six normal adult rat renal glomerular networks and use their reeb graphs already constructed and published in the literature. We observe that five of them contain internal vertices of degree higher than three. Branch points in vascular networks may appear to be of a higher degree if the imaging resolution cannot differentiate between blood vessels that are very close in proximity. Here, we propose a random graph theory model that edits a non-cubic vascular network into a cubic graph. We observe that the edited cubic graph from a non-cubic vascular network has the similar size and order as the one cubic vascular network.