Abstract
This work presents an efficient and automated method to extract the human cerebral ventricular system from MRI driven by anatomic knowledge. The ventricular system is divided into six three-dimensional regions; six ROIs are defined based on the anatomy and literature studies regarding variability of the cerebral ventricular system. The distribution histogram of radiological properties is calculated in each ROI, and the intensity thresholds for extracting each region are automatically determined. Intensity inhomogeneities are accounted for by adjusting intensity threshold to match local situation. The extracting method is based on region-growing and anatomical knowledge, and is designed to include all ventricular parts, even if they appear unconnected on the image. The ventricle extraction method was implemented on the Window platform using C++, and was validated qualitatively on 30 MRI studies with variable parameters.
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
Schnack, H.G., Hulshoff, P.H.E., Baare, W.F.C., Viergever, M.A., Kahn, R.S.: Automatic segmentation of the ventricular system from MR images of the human brain. NeuroImage 14, 95–104 (2001)
Worth, A.J., Makris, N., Patti, M.R., Goodman, J.M., Hoge, E.A., Caviness, V.S., Kennedy, D.N.: Precise segmentation of the lateral ventricles and caudate nucleus in MR brain images using anatomically driven histograms. IEEE Transactions on Medical Imaging 17(2), 303–310 (1998)
Kaus, M.R., Warfield, S.K., Nabavi, A., Black, P.M., Jolesz, F.A., Kikinis, R.: Automated segmentation of MR images of brain tumors. Radiology 218(2), 586–591 (2001)
Holden, M., Schnable, J.A., Hill, D.L.G.: Quantifying small changes in brain ventricular volume using non-rigid registration. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 49–56. Springer, Heidelberg (2001)
Baillard, C., Hellier, P., Barillot, C.: Segmentation of 3D brain structures using level sets and dense registration. In: IEEE Workshop on Mathematical Methods on Biomedical Image Analysis (MMBIA 2000), pp. 94–101 (2000)
Wang, Y., Staib, L.H.: Boundary finding with correspondence using statistical shape models. In: Proceeding IEEE conference of computer vision and pattern recognition, pp. 338–345 (1998)
Sonka, M., Tadikonda, S.K., Collins, S.M.: Knowledge-based interpretation of MR brain images. IEEE Transactions on Medical Imaging 15(4), 443–452 (1996)
Center for Medical Diagnostic Systems and Visualisation, University of Bremen, http://www.mevis.de/projects/volumetry/volumetry.html
Fisher, E., Rudick, R.A.: Method and system for brain volume analysis. USA patent US006366797B1 (2002)
Hu, Q., Nowinski, W.L.: Method and apparatus for determining symmetry in 2D and 3D images. PCT patent application (PCT/SG02/00006) (January 2002)
Nowinski, W.L.: Modified Talairach landmarks. Acta Neurochirurgica 143(10), 1045–1057 (2001)
Newton, T.H., Potts, D. (eds.): Radiology of the skull and brain, ventricles and cisterns, pp. 3494–3537. MediBooks, Great Neck
Robertson, E.G: Pneumoencephalography, 2nd edn. Springfield Il. Thomas CC Publisher (1967)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xia, Y., Hu, Q., Aziz, A., Nowinski, W.L. (2003). Knowledge-Driven Automated Extraction of the Human Cerebral Ventricular System from MR Images. In: Taylor, C., Noble, J.A. (eds) Information Processing in Medical Imaging. IPMI 2003. Lecture Notes in Computer Science, vol 2732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45087-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-45087-0_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40560-3
Online ISBN: 978-3-540-45087-0
eBook Packages: Springer Book Archive