Abstract
An automated method for orientation of functional brain image is proposed. Intrinsec information is captured from the image in three stages: first the volume to identify the anterior to posterior line, second the symmetry to detect the hemisphere dividing plane and third the contour to determine the up-down and front-back orientation. The approach is tested in more than a tousand images from different formats and modalities with high reconition rates.
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
Preview
Unable to display preview. Download preview PDF.
References
Christensen, J.D., Hutchins, G.C., McDonald, C.J.: Computer automated detection of head orientation for prevention of wrong-side treatment errors. In: AMIA Annual Symposium Proceedings, pp. 136–140 (2006) PMID: 17238318 PMCID: PMC1839503
Tsao, J., Stundzia, A., Ichise, M.: Fully automated establishment of stereotaxic image orientation in six degrees of freedom for technetium-99m-ECD brain SPECT. Journal of nuclear medicine: official publication, Society of Nuclear Medicine 39(3), 503–508 (1998) PMID: 9529300
Prima, S., Ourselin, S., Ayache, N.: Computation of the mid-sagittal plane in 3-d brain images. IEEE Transactions on Medical Imaging 21(2), 122–138 (2002)
Ardekani, B., Kershaw, J., Braun, M., Kanuo, I.: Automatic detection of the mid-sagittal plane in 3-d brain images. IEEE Transactions on Medical Imaging 16(6) (December 1997)
Hu, Q., Nowinski, W.L.: A rapid algorithm for robust and automatic extraction of the midsagittal plane of the human cerebrum from neuroimages based on local symmetry and outlier removal. NeuroImage 20(4), 2153–2165 (2003)
Ardekani, B.A., Bachman, A.H.: Model-based automatic detection of the anterior and posterior commissures on MRI scans. NeuroImage 46(3), 677–682 (2009) PMID: 19264138
Liu, Y., Collins, R., Rothfus, W.: Robust midsagittal plane extraction from normal and pathological 3-d neuroradiology images. IEEE Transactions on Medical Imaging 20(3), 175–192 (2001)
Junck, L., Moen, J.G., Hutchins, G.D., Brown, M.B., Kuhl, D.E.: Correlation methods for the centering, rotation, and alignment of functional brain images. Journal of Nuclear Medicine 31(7), 1220–1226 (1990)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9(1), 62–66 (1979)
von Hippel, P.T.: Mean, median, and skew: Correcting a textbook rule. Journal of Statistics Education 13(2) (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Illán, I.A. et al. (2013). Automatic Orientation of Functional Brain Images for Multiplataform Software. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds) Natural and Artificial Models in Computation and Biology. IWINAC 2013. Lecture Notes in Computer Science, vol 7930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38637-4_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-38637-4_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38636-7
Online ISBN: 978-3-642-38637-4
eBook Packages: Computer ScienceComputer Science (R0)