An ontology approach for classification of abnormal white matter in patients with multiple sclerosis

Authors:
Bruno Alfano;Arturo Brunetti;Giuseppe De Pietro;Amalia Esposito
Affiliations:
National Research Council, Biostructure and Bioimaging Institute, Napoli, Italy;University "Federico II", Department of Biomorphological and Functional Sciences, Napoli, Italy;National Research Council, Institute for High-Performance Computing and Networking, Napoli, Italy;National Research Council, Institute for High-Performance Computing and Networking, Napoli, Italy
Venue:
USAB'07 Proceedings of the 3rd Human-computer interaction and usability engineering of the Austrian computer society conference on HCI and usability for medicine and health care
Year:
2007

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Abstract

Multiple Sclerosis (MS) is an inflammatory autoimmune disease of the Central Nervous System, characterized by development of lesions that cause interference in the communication between brain and the rest of the body. Some techniques using numeric algorithms based on mathematical and probabilistic theories are generally used in order to obtain lesions detection. In this paper we describe an innovative approach for lesions recognition to be applied after segmentation of brain tissues from quantitive evaluation of MR studies. Knowledge about MS lesions is formalized through an ontology and a set of rules: integrating them, automatic inferences can be realized to point out lesions, starting from data about potentially brain abnormal white matter.