Section: New Results
Management of Information in Neuroimaging
Integration of Heterogeneous and Distributed Information in Neuroimaging
Our current participation within the NeuroLOG project concerned the first work package especially on the elaboration of the proposed system architecture, the second work package on the finalization of the "OntoNeurobase" and on the Application work package for the specification of the MS meta-data. In addition we ware responsible for the integration of the technical report L3 dedicated to the overall specification of the NeuroLOG architecture.
Ontology of Datasets and Image processing tools in neuroimaging
Based on preliminary work done during the exploratory phase of the Neurobase project, we have refined the Neurobase ontology toward a more formal and more modular ontology, called OntoNeurobase. This ontology is based on a multi-layered, modular approach to ontology design. We used DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering) as a foundational ontology, providing the common philosophical foundation, together with several core ontologies, namely I&DA (Information and Discourse Acts) for modeling documents (texts and images), COPS (Core Ontology of Programs and Software) for modeling programs and software , and OntoKADS for modeling problem solving activities. Our original contribution concerns Datasets and Image processing tools in the field of neuroimaging. Our most recent work concerned (1) the hierarchy of datasets, (2) the representation of mathematical functions modeling image data, (3) image annotations relating regions of interest defined on the images to real world entities denoting the anatomy or physiology of the subject, and (4) a taxonomy of image processing. This work has been done in collaboration with Michel Dojat (INSERM U594) and Gilles Kassel and his colleagues from LaRIA (CNRS FRE 2733) in Amiens.
Ontologies for modeling brain structures in neuroimaging applications
We are interested in showing how such information can complement other kinds of priors such as statistical probability maps. In our system, probability maps provide a first set of assumptions about brain gyri, from which the system automatically derives a series of assumptions concerning sulci. Image annotations are obtained through a cooperation with the user, who can enforce specific labels for particular gyri and sulci. The system iterates reasoning, which limits the range of labelling possibilities. Resulting annotations are then stored as OWL instance files. The system uses an OWL ontology of brain cortical structures (focusing of the representation of part-of and topology relationships) and of rules (represented in SWRL), modelling complex dependencies between those relationships. Merging these two kinds of knowledge is made using the KAON2 reasoner (http://www.aifb.uni-karlsruhe.de/Projekte/viewProjekt?id_db=62 ). This work has been done in collaboration with Pr Christine Golbreich (University of Versailles Saint Quentin), who is co-supervisor of Ammar Mechouche’s PhD thesis.