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.
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References
Pirko, I., Lucchinetti, C.F., Sriram, S., Bakshi, R.: Gray matter involvement in multiple sclerosis. Neurology 68, 634–642 (2007)
McDonald, W.I, Compston, A., Edan, G., et al.: Recommended Diagnostic Criteria for Multiple Sclerosis. Annals of Neurology 50, 121–127 (2001)
Holzinger, A., Geierhofer, R., Errath, M.: Semantic Information in Medical Information Systems - from Data and Information to Knowledge: Facing Information Overload. In: Proc. of I-MEDIA 2007 and I-SEMANTICS 2007, Graz, Austria, pp. 323–330 (2007)
Podgorelec, V., Pavlic, L.: Managing Diagnostic Process Data Using Semantic Web. In: CBMS 2007. Twentieth IEEE International Symposium on Computer-Based Medical Systems, IEEE Computer Society Press, Los Alamitos (2007)
Rosse, C., Mejino, J.L.V.: A Reference Ontology for Bioinformatics: The Foundational Model of Anatomy. Journal of Biomedical Informatics 36, 478–500 (2003)
Rector, A.L., Rogers, J.E., Zanstra, P.E., Van Der Haring, E.: OpenGALEN: Open Source Medical Terminology and Tools. In: Proc AMIA Symp., p. 982 (2003)
Humphreys, B., Lindberg, D.: The UMLS project: making the conceptual connection between users and the information they need. Bulletin of the Medical Library Association 81(2), 170–177 (1993)
Golbeck, J., Fragoso, G., Hartel, F., Hendler, J., Parsia, B., Oberthaler, J.: The national cancer institute’s thesaurus and ontology. Journal of Web Semantics 1 (2003)
Open Biomedical Ontologies, available at http://www.obofoundry.org
Holzinger, A.: Usability Engineering for Software Developers. Communications of the ACM 48(1), 71–74 (2005)
Kumar, A.Y., Lina Yip, Y., Smith, B., Marwede, D., Novotny, D.: An Ontology for Carcinoma Classification for Clinical Bioinformatics.Medical Informatics Europe (MIE 2005), Geneva, 635–640 (2005)
Golbreich, C., Bouet, M.: Classification des compte-rendus mammographiques a partir d’une ontologie radiologique en OWL. In: Extraction et gestion de Connaissances (EGC’2006), RNTI, vol. 1, pp. 199–204 (January 2006)
Cosic, D., Loncaric, S.: Rule-Based Labeling of CT Head Image. In: Keravnou, E.T., Baud, R.H., Garbay, C., Wyatt, J.C. (eds.) AIME 1997. LNCS, vol. 1211, Springer, Heidelberg (1997)
Mechouche, A., Golbreich, C., Gibaud, B.: Towards an hybrid system for annotating brain MRI images. In: CEUR. Proceedings of the OWLED 2006 Workshop on OWL: Experiences and Directions, Athens, Georgia, USA, November 10-11, 2006, vol. 216, pp. 10–11 (2006)
Alfano, B., Brunetti, A., Larobina, M., Quarantelli, M., Tedeschi, E., Ciarmiello, A., Covelli, E., Salvatore, M.: Automated Segmentation and Measurement of Global White Matter Lesion Volume in Patients with Multiple Sclerosis. Journal of Magnetic Resonance Imaging 12, 799–807 (2000)
Shahar, A., Greenspan, H.: A Probabilistic Framework for the Detection and Tracking in Time of Multiple Sclerosis Lesions, Macro to Nano, 2004. In: IEEE International Symposium on Biomedical Imaging, pp. 440–443. IEEE Computer Society Press, Los Alamitos (2004)
Boudra, A., Dehak, R., Zhu, Y.M., Pachai, C., Bao, Y.G., Grimaud, J.: Automated segmentation of multiple sclerosis lesions in multispectral magnetic resonance imaging using fuzzy clustering. Computers in Biology and Medicine 30(1), 23–40 (2000)
Ganna, M., Rombaut, M., Goutte, R., Zhu, Y.M.: Improvement of brain lesions detection using information fusion approach. In: 6th International Conference on Signal Processing (2002)
McGuinness, D.L., van Harmelen, F.: OWL Web Ontology Language Overview, W3C Recommendation (February 10, 2004). Latest version is available at http://www.w3c.org/TR/owl-features
Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M.: SWRL: A Semantic Web Rule Language Combining OWL and RuleML, W3C Member (May 21, 2004) (submission).Latest version is available at http://www.w3.org/Submission/SWRL
Gruber, T.: Towards Principles for the Design of Ontologies Used for Knowledge Sharing. International Journal of Human Computer Studies, 907–928 (1995)
Dameron, O.: Modélisation, représentation et partage de connaissances anatomiques sur le cortex cérébral, Thése de doctorat d’Université, Université de Rennes 1 (2003)
Alfano, B., Brunetti, A., Arpaia, M., Ciarmiello, A., Covelli, E.M., Salvatore, M.: Multiparametric Display of Spin-Echo Data from MR Studies of Brain. Journal of Magnetic Resonance Imaging 5(2), 217–225 (1995)
KAON2 , http://kaon2.semanticweb.org/
Motik, B., Harrocks, I., Rosati, R., Seattler, U.: Can OWL and logic programming live together happily ever after? In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 501–514. Springer, Heidelberg (2006)
Prud’hommeaux, E., Seaborne, A., SPARQL,: Query Language for RDF, W3C Candidate Recommendation (June 14, 2007). Latest version is available at http://www.w3.org/TR/rdf-sparql-query
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Alfano, B., Brunetti, A., De Pietro, G., Esposito, A. (2007). An Ontology Approach for Classification of Abnormal White Matter in Patients with Multiple Sclerosis. In: Holzinger, A. (eds) HCI and Usability for Medicine and Health Care. USAB 2007. Lecture Notes in Computer Science, vol 4799. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76805-0_34
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DOI: https://doi.org/10.1007/978-3-540-76805-0_34
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