Abstract
The management system and knowledge extraction of bioimaging in the cloud (R & D Cloud CEIB) which is proposed in this article will use the services offered by the centralization of bioimaging through Valencian Biobank Medical Imaging (GIMC in Spanish) as a basis for managing and extracting knowledge from a bioimaging bank, providing that knowledge as services with high added value and expertise to the Electronic Patient History System (HSE), thus bringing the results of R & D to the patient, improving the quality of the information contained therein. R & D Cloud CEIB has four general modules: Search engine (SE), manager of clinical trials (GEBID), anonymizer (ANON) and motor knowledge (BIKE). The BIKE is the central module and through its sub modules analyses and generates knowledge to provide to the HSE through services. The technology used in R & D Cloud CEIB is completely based on Open Source.
Within the BIKE, we focus on the development of the classifier module (BIKEClassifier), which aims to establish a method for the extraction of biomarkers for bioimaging and subsequent analysis to obtain a classification in bioimaging available pools following GIMC diagnostic experience.
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References
FSL Group, http://www.fmrib.ox.ac.uk/fsl/
Jenkinson, M., Beckmann, C.F., Behrens, T.E.J., Woolrich, M.W., Smith, S.M.: FSL. NeuroImage (2011) (in press)
Woolrich, M.W., Jbabdi, S., Patenaude, B., Chappell, M., Makni, S., Behrens, T., Beckmann, C., Jenkinson, M., Smith, S.M.: Bayesian analysis of neuroimaging data in FSL. NeuroImage 45, S173–S186 (2009)
Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E.J., Johansen-Berg, H., Bannister, P.R., De Luca, M., Drobnjak, I., Flitney, D.E., Niazy, R., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J.M., Matthews, P.M.: Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23(S1), 208–219 (2004)
caBIG Community Website, https://cabig.nci.nih.gov/
Rex, D.E., Ma, J.Q., Toga, A.W.: The LONI Pipeline Processing Environment. Neuroimage 19(3), 1033–1048 (2003)
Dinov, I.D., Lozev, K., Petrosyan, P., Liu, Z., Eggert, P., Pierce, J., Zamanyan, A., Chakrapani, S., Van Horn, J.D., Parker, D.S., Magsipoc, R., Leung, K., Gutman, B., Woods, R.P., Toga, A.W.: Neuroimaging Study Designs, Computational Analyses and Data Provenance Using the LONI Pipeline. PLoS ONEÂ 5(9), e13070 (2010), doi:10.1371/journal.pone.0013070
XNAT - Open source informatics for biomedical imaging research, http://www.xnat.org
Marcus, D.S., Olsen, T., Ramaratnam, M., Buckner, R.L.: The Extensible Neuroimaging Archive Toolkit (XNAT): An informatics platform for managing, exploring, and sharing neuroimaging data. Neuroinformatics 5(1), 11–34 (2007)
Manjon, J.V., Marti-Bonmati, L., Robles, M., Celda, B.: Postproceso en Imagen Medica: morfologia, funcional y molecular
Marti Bonmati, L., Alberich-Bayarri, A., Garcia-Marti, G., Sanz Requena, R., Perez Castillo, C., Carot Sierra, J.M., Manjon Herrera, J.V.: Biomarcadores de imagen, imagen cuantitativa y bioingenieria. In: Radiologia 2011 (2011)
DICOM, http://dicom.nema.org/
Downing, G.: Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints. Clin. Pharmacol. Therap. 69, 89–95 (2001)
Schuster, D.: The opportunities and challenges of developing imaging biomarkers to study lung function and disease. Am. J. Respir. Crit. Care Med. 176, 22–30 (2007)
Van Beers, B., Cuenod, C.A., Mart-Bonmat, L., Matos, C., Niessen, W., Padhani, A.: European Society of Radiology Working Group on Imaging Biomarkers. White paper on Imaging Biomarkers. Insights Imaging. 1, 42–45 (2010)
Campbell, C.: Kernel methods: a survey of current techniques. Neurocomputing 48, 63–84 (2002)
Mavroforakis, M.E., Georgiou, H.V., Dimitropoulos, N., Cavouras, D., Theodoridis, S.: Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers. Artif Intell Med. 37(2), 145–162 (2006)
Siuly, Li, Y., Wen, P.P.: Clustering technique-based least square support vector machine for EEG signal classification. Comput Methods Programs Biomed. 104(3), 358–372 (2011)
Bonev, B., Escolano, F., Cazorla, M.: Feature selection, mutual information, and the classification of high-dimensional patterns: Applications to image classification and microarray data analysis. Pattern Analysis and Applications 11(3-4) (August 2008)
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Salinas, J.M., de la Iglesia-Vaya, M., Bonmati, L.M., Valenzuela, R., Cazorla, M. (2012). R & D Cloud CEIB: Management System and Knowledge Extraction for Bioimaging in the Cloud. In: Omatu, S., De Paz Santana, J., González, S., Molina, J., Bernardos, A., RodrÃguez, J. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28765-7_39
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DOI: https://doi.org/10.1007/978-3-642-28765-7_39
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