A support system for content-based medical image retrieval in object oriented databases.

This work describes both the concepts used in an Object Manager for storage of medical images as one more data type associated to objects, and a support system developed to offer this kind of tool to medical application developers. The purpose of this work is to support the retrieval of images through queries based on the graphical contents of the stored images. The usual approach uses icons and textual attributes stored with the images to specify the queries. This work uses a novel modeling technique to define the "image data type," by means of which it is possible to decide, before the query itself, the key data of each image that must be extracted from the image when it is stored in the database, so the search can be accelerated when queries are issued. This approach enables building of expansible systems, where new image processing algorithms can be added easily, using its syntactic representation stored through an Image Meta-schema into the application database schema. This work shows how such a system has been implemented, and also provides a query language used to refer and execute these algorithms from inside the database management system.

3D reconstruction of tomographic images applied to largely spaced slices.

This paper presents a full reconstruction process of magnetic resonance images. The first step is to bring the acquired data from the frequency domain, using a Fast Fourier Transform algorithm. A Tomographic Image Interpolation is then used to transform a sequence of tomographic slices in an isotropic volume data set, a process also called 3D Reconstruction. This work describes an automatic method whose interpolation stage is based on a previous matching stage using Delaunay Triangulation. The reconstruction approach uses an extrapolation procedure that permits appropriate treatment of the boundaries of the object under analysis.