Abstract
In Voxel-Based Morphometry (VBM), spatial normalisation is a major process which transforms images into a standard space and is often referred to as co-registration. This project is a comparison and observation of differences in the performance, measured as the overlap between images, of two co-registration algorithms used in VBM on human brain Magnetic Resonance Imaging (MRI) data. Here we show differences between genders and algorithms on specific regions of the brain using grey matter segments and unsegmented MRI images. Results show that there are significant differences in the overlap of regions depending on the algorithm which may be considered in addition to current knowledge on the subject. Importantly, we are interested in investigating what these differences mean to published and on-going research as well as observing whether said difference spans all the way to the Parahippocampal Gyrus and other important regions associated with psychological related diseases.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Shen, S., Sterr, A., Szameitat, A.: A template effect study on voxel-based morphometry in statistic parametric mapping. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 3051–3054 (2005). https://doi.org/10.1109/IEMBS.2005.1617118
Zhang, J., Buchsbaum, M.S., Chu K., Hazlett, E.A.: Comparison between voxel-based morphometry and volumetric analysis in schizophrenia. In: 2008 International Conference on BioMedical Engineering and Informatics, vol. 2, pp. 48–52 (2008). https://doi.org/10.1109/BMEI.2008.208
Melonakos, E., Shenton, M., Rathi, Y., Terry, D., Bouix, S., Kubicki, M.: Voxel-based morphometry (VBM) studies in schizophrenia–can white matter changes be reliably detected with VBM? Psychiatry Res. 193(2), 65–70 (2012). https://doi.org/10.1016/j.pscychresns.2011.01.009
McNabb, C.B., et al.: Functional network dysconnectivity as a biomarker of treatment resistance in schizophrenia. Schizophr. Res. 195, 160–167 (2017). https://doi.org/10.1016/j.schres.2017.10.015
Jenkinson, M., Beckmann, C.F., Behrens, T.E.J., Woolrich, M.W., Smith, S.M.: FSL. NeuroImage 62, 782–790 (2012). https://doi.org/10.1016/j.neuroimage.2011.09.015
Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12, 26–41 (2008). https://doi.org/10.1016/j.media.2007.06.004
Maurer, C.R., Fitzpatrick, J.M., Wang, M.Y., Galloway, R.L., Maciunas, R.J., Allen, G.S.: Registration of head volume images using implantable fiducial markers. IEEE Trans. Med. Imaging 16, 447–462 (1997). https://doi.org/10.1109/42.611354
Tait, R.J., Schaefer, G., Hopgood, A., Nakashima, T.: High performance medical image registration using a distributed blackboard architecture. In: Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007, pp. 252–257 (2007)
Eggert, L.D., Sommer, J., Jansen, A., Kircher, T., Konrad, C.: Accuracy and reliability of automated gray matter segmentation pathways on real and simulated structural magnetic resonance images of the human brain. PloS ONE 7(9), e45081 (2012). https://doi.org/10.1371/journal.pone.0045081
Radua, J., Canales-RodrÃguez, E.J., Pomarol-Clotet, E., Salvador, R.: Validity of modulation and optimal settings for advanced voxel-based morphometry. Neuroimage 86, 81–90 (2014)
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945). https://doi.org/10.2307/1932409
Zevin, J.: Encyclopedia of Neuroscience, pp. 517–522. Academic Press (2009). https://doi.org/10.1016/B978-008045046-9.01881-7
Stark, S.M., Stark, C.E.L.: Neurobiology of language. In: Introduction to Memory, Chap. 67, pp. 841–854 (2016). https://doi.org/10.1016/B978-0-12-407794-2.00067-5
Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54, 2033–2044 (2011). https://doi.org/10.1016/j.neuroimage.2010.09.025
IBSR Data Set v2.0. https://www.nitrc.org/frs/shownotes.php?release_id=2316
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Svejda, M., Tait, R. (2019). Characterisation of VBM Algorithms for Processing of Medical MRI Images. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXVI. SGAI 2019. Lecture Notes in Computer Science(), vol 11927. Springer, Cham. https://doi.org/10.1007/978-3-030-34885-4_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-34885-4_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34884-7
Online ISBN: 978-3-030-34885-4
eBook Packages: Computer ScienceComputer Science (R0)