Abstract
Human brain imaging techniques, such as Magnetic Resonance Imaging (MRI) or Diffusion Tensor Imaging (DTI), have been established as scientific and diagnostic tools and their adoption is growing in popularity. Statistical methods, machine learning and data mining algorithms have successfully been adopted to extract predictive and descriptive models from neuroimage data. However, the knowledge discovery process typically requires also the adoption of pre-processing, post-processing and visualisation techniques in complex data workflows. Currently, a main problem for the integrated preprocessing and mining of MRI data is the lack of comprehensive platforms able to avoid the manual invocation of preprocessing and mining tools, that yields to an error-prone and inefficient process. In this work we present K-Surfer, a novel plug-in of the Konstanz Information Miner (KNIME) workbench, that automatizes the preprocessing of brain images and leverages the mining capabilities of KNIME in an integrated way. K-Surfer supports the importing, filtering, merging and pre-processing of neuroimage data from FreeSurfer, a tool for human brain MRI feature extraction and interpretation. K-Surfer automatizes the steps for importing FreeSurfer data, reducing time costs, eliminating human errors and enabling the design of complex analytics workflow for neuroimage data by leveraging the rich functionalities available in the KNIME workbench.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cannataro, M., Guzzi, P.H., Sarica, A.: Data mining and life sciences applications on the grid. WIREs Data Mining Knowl. Discov. 3, 216–238 (2013)
Megalooikonomou, V., Ford, J., Shen, L., Makedon, F., Saykin, A.: Data Mining in Brain Imaging. Stat Methods Med. Res. 9, 359–394 (2000)
Sarica, A., Cerasa, A., Vasta, R., Perrotta, P., Valentino, P., Mangone, G., Guzzi, P.H., Rocca, F., Nonnis, M., Cannataro, M., Quattrone, A.: Tractography in amyotrophic lateral sclerosis using a novel probabilistic tool: A study with tract-based reconstruction compared to voxel-based approach. Journal of Neuroscience Methods 224, 79–87 (2014)
Berthold, M., Cebron, N., Dill, F., Di Fatta, G., Gabriel, T., Georg, F., Meinl, T., Ohl, P., Sieb, C., Wiswedel, B.: KNIME: the Konstanz Information Miner. In: Proceedings of the Workshop on Multi-Agent Systems and Simulation (MAS&S), 4th Annual Industrial Simulation Conference (ISC), Palermo, Italy, June 5-7, pp. 58–61 (2006)
Berthold, M.R., Cebron, N., Dill, F., Gabriel, T.R., Kotter, T., Meinl, T., Ohl, P., Thiel, K., Wiswedel, B.: KNIME - the Konstanz Information Miner: Version 2.0 and Beyond. SIGKDD Explor. Newsl. 11, 26–31 (2009)
Symms, M., Jager, H.R., Schmierer, K., Yousry, T.A.: ’A Review of Structural Magnetic Resonance Neuroimaging’. J. Neurol. Neurosurg. Psychiatry 75, 1235–1244 (2004)
Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.P., Frith, C.D., Frackowiak, R.S.J.: Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2, 189–210 (1994)
Goebel, R.: Brainvoyager–Past, Present, Future. Neuroimage 62, 748–756 (2012)
Van Essen, D.C., Dickson, J., Harwell, J., Hanlon, D., Anderson, C.H., Drury, H.A.: An Integrated Software System for Surface-based Analyses of Cerebral Cortex. Journal of American Medical Informatics Association 8(5), 443–459 (2001)
Pieper, S., Halle, M., Kikinis, R.: 3D SLICER. In: Proc. IEEE Int. Symp. Biomed. Imaging., pp. 632–635 (2004)
Cook, P.A., Bai, Y., Nedjati-Gilani, S., Seunarine, K.K., Hall, M.G., Parker, G.J., Alexander, D.C.: Camino: Open-Source Diffusion-MRI Reconstruction and Processing. In: 14th Scientific Meeting of the International Society for Magnetic Resonance in Medicine, Seattle, WA, USA, pp. 27–59 (May 2006)
Dale, A.M., Fischl, B., Sereno, M.I.: Cortical surface-based analysis. I. segmentation and surface reconstruction. NeuroImage 9, 179–194 (1999)
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)
Rex, D.E., Ma, J.Q., Toga, A.W.: The Loni Pipeline Processing Environment. Neuroimage 19, 1033–1048 (2003)
Rexer, K.: Rexer Analytics 2013 Data Miner Survey (2013), http://www.rexeranalytics.com/Data-Miner-Survey-2013-Intro.html
Sarica, A., Critelli, C., Guzzi, P.H., Cerasa, A., Quattrone, A., Cannataro, M.: Application of Different Classification Techniques on Brain Morphological Data. In: Proc. of the 26th IEEE Inter. Symposium on Computer-based Medical Systems (CBMS), Porto, Portugal, pp. 425–428 (June 2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Sarica, A., Di Fatta, G., Cannataro, M. (2014). K-Surfer: A KNIME Extension for the Management and Analysis of Human Brain MRI FreeSurfer/FSL Data. In: Ślȩzak, D., Tan, AH., Peters, J.F., Schwabe, L. (eds) Brain Informatics and Health. BIH 2014. Lecture Notes in Computer Science(), vol 8609. Springer, Cham. https://doi.org/10.1007/978-3-319-09891-3_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-09891-3_44
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09890-6
Online ISBN: 978-3-319-09891-3
eBook Packages: Computer ScienceComputer Science (R0)