Semantic localization-driven partial image retrieval in CT series.

BACKGROUND: Picture archiving and communication systems (PACS) contain very large amounts of computed tomography (CT) data. When querying a PACS for a particular series, the user is often not interested in the complete series but in a certain region of interest (ROI), described e.g. by an example view in another series or an anatomical concept. OBJECTIVES: Restricting a retrieval query to such an ROI saves both loading time and navigational effort. In this paper, we propose an efficient method for defining and retrieving ROIs. METHODS: We employ interpolation and regression techniques for mapping the slices of a series to a newly generated standardized height atlas of the human body. RESULTS: Examinations of the accuracy and the saved input/output (I/O) costs of our new method on a repository of 1,360 CT series demonstrate the advantages of our system. Depending on the scope of the retrieval query, we can economize up to 99% of the total loading time. CONCLUSION: Our proposed method for flexible, context-based, partial image retrieval enables the user to directly focus on the relevant portion of the image material and it targets the high potential of I/O cost reduction of a common PACS.

Nearest neighbor classification in 3D protein databases.

In molecular databases, structural classification is a basic task that can be successfully approached by nearest neighbor methods. The underlying similarity models consider spatial properties such as shape and extension as well as thematic attributes. We introduce 3D shape histograms as an intuitive and powerful approach to model similarity for solid objects such as molecules. Errors of measurement, sampling, and numerical rounding may result in small displacements of atomic coordinates. These effects may be handled by using quadratic form distance functions. An efficient processing of similarity queries based on quadratic forms is supported by a filter-refinement architecture. Experiments on our 3D protein database demonstrate the high classification accuracy of more than 90% and the good performance of the technique.

Solvent accessible surface representation in a database system for protein docking.

Protein docking is a new and challenging application for query processing in database systems. Our architecture for an efficient support of docking queries is based on the multistep query processing paradigm, a technique well-known from spatial database system. Along with physicochemical parameters, the geometry of the molecules plays a fundamental role for docking retrieval. Thus, 3D structures and 3D surfaces of molecules are basic objects in molecular databases. We specify a molecular surface representation based on topology, define a class of neighborhood queries, and sketch some applications with respect to the docking problem. We suggest a patch-based data structure called the TriEdge structure, first, to efficiently support topological query processing, and second, to save space in comparison to common planar graph representations such as the quad-edge structure. In analogy to the quad-edge structure, the TriEdge structure has an algebraic interface and is implemented via complex pointers. However, we achieve a reduction of the space requirement by a factor of four. Finally, we investigate the time performance of our prototype.