Abstract
The work described in this document is part of a major work aiming at a complete pipeline for the extraction of clinical parameters from MR images of the brain, for the diagnosis of neuro-degenerative diseases. A key step in this pipeline is the identification of a box containing the hippocampus and surrounding medial temporal lobe regions from T1-weighted magnetic resonance images, with no interactive input from the user. To this end we introduced in the existing pipeline a module for the segmentation of brain tissues based on a constrained Gaussians mixture model (CGMM), and a novel method for generating templates of the hippocampus. The templates are then combined in order to obtain only one template mask. This template mask is used, with a mask of the grey matter of the brain, for determining the hippocampus. The results have been visually evaluated by a small set of experts, and have been judged as satisfactory. A complete and exhaustive evaluation of the whole system is being planned.
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
References
Atiya, M., Hyman, B., Albert, M., Killiany, R.: Structural magnetic resonance imaging in established and prodroaml Alzheimer disease: a review. Alz. Dis. Assoc. Dis. 17(3), 177–195 (2003)
Braak, H., Braak, E.: Neuropathological staging of Alzheimer-related changes. Acta. Neuropathol. 82, 239–259 (1991)
Calvini, P., Chincarini, A., Donadio, S., Gemme, G., Squarcia, S., Nobili, F.N., Rodriguez, G., Bellotti, R., Catanzariti, E., Cerello, P., Mitri, I.D., Fantacci, M.: Automatic localization of the hippocampal region in mr images to asses early diagnosis of Alzheimer’s disease in MCI patients. In: Nuclear Science Symposium Conference Record, pp. 4348–4354 (2008)
Calvini, P., Chincarini, A., Gemme, G., Penco, M., Squarcia, S., Nobili, F., Rodriguez, G., Bellotti, R., Catanzariti, E., Cerello, P., Mitri, I.D., Fantacci, M.: Automatic analysis of medial temporal lobe atrophy from structural MRIs for the early assessment of Alzheimer disease. Med. Phys. 36(8), 3737–3747 (2009)
Chetelat, G., Baron, J.: Early diagnosis of Alzheimer disease: contribution of structural neuroimaging. Neuoroimage 18(2), 525–541 (2003)
Chupin, M., Chételat, G., Lemieux, L., Dubois, B., Garnero, L., Benali, H., Eustache, F., Lehéricy, S., Desgranges, B., Colliot, O.: Fully automatic hippocampus segmentation discriminates between early Alzheimer’s disease and normal aging. In: Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 97–100 (2008)
Dubois, B., Albert, M.: Amnestic MCI or prodromal Alzheimer disease? Lancet Neurol. 3(4), 246–248 (2004)
Duchesne, S., Pruessner, J.C., Collins, D.L.: Appearance-based segmentation of medial temporal lobe structures. Neuroimage 17(2), 515–531 (2002)
Evans, A., Kambler, M., Collins, D., MacDonald, D.: An MRI-based probabilistic atlas of neuroanatomy. In: Shorvon, S., Fish, D., Andermann, F., Bydder, G., Stefan, H. (eds.) Magnetic Resonance Scanning and Epilepsy. NATO ASI Series A, Life Sciences, vol. 264, pp. 263–274. Plenum Press, New York (1994)
Greenspan, H., Ruf, A., Goldberger, J.: Constrained Gaussian mixture model framework for automatic segmentation of MR brain images. IEEE Trans. Med. Imag. 25(9), 1233–1245 (2006)
Joshi, S., Pizer, S., Fletcher, P., Yushkevich, P., Thall, A., Marron, J.: Multiscale deformable model segmentation and statistical shape analysis using medial description. IEEE Trans. Med. Imag. 21(5), 538–550 (2002)
Kantarci, K., Jack, C.: Neuroimaging in Alzheimer disease: an evidence based review. Neuroimag. Clin. N. Am. 13(2), 197–209 (2003)
Kass, W., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)
Kelemen, A., Székely, G., Gerig, G.: Elastic model-based segmentation of 3D neurological data sets. IEEE Trans. Med. Imag. 18(10), 828–839 (1999)
Lee, W., Gow, K., Zhao, Y., Staff, R.: Hybrid segmentation of the hippocampus in mr images. In: Proceedings of the 13th European Signal Processing Conference, Antalya, Turkey (2005)
Miller, J., Lorensen, W., O’Bara, R., Wozny, M.: Geometrically deformed models: A method for extracting closed geometric models from volume data. SIGGRAPH Comput. Graph. 25, 217–226 (1991)
Petersen, R., Smith, G., Waring, S., Tangalos, E., Kokmen, E.: Mild cognitive impairment: clinical characterization and outcome. Arch. Neurol. 56(3), 303–308 (1999)
Shen, D., Moffat, S., Resnick, S.M., Davatzikos, C.: Measuring size and shape of the hippocampus in MR images using a deformable shape model. NeuroImage 15, 422–434 (2002)
Thirion, J.: Image matching as a diffusion process: an analogy with maxwell’s demons. Med. Image Anal. 2(3), 243–260 (1998)
Thompson, P., Hayashi, K., deZubicaray, G., Janke, A., Rose, S., Semple, J., Hong, M., Herman, D., Gravano, D., Doddrell, D., Toga, A.: Mapping hippocampal and ventricular change in Alzheimers’s disease. Neuroimage 22(4), 1754–1766 (2004)
Warfield, S., Zou, K., Wells, W.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imag. 23, 903–921 (2004)
Winblad, B., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L., Nordberg, A., Backman, L., Albert, M., Almkvist, O., Arai, H., Basun, H., Blennow, K., deLeon, M., DeCarli, C., Erkinjuntti, T., Giacobini, E., Graff, C., Hardy, J., Jack, C., Jorm, A., Ritchie, K., van Duijn, C., Visser, P., Petersen, R.: Mild cognitive impairment–beyond controversies, towards a consensus: report of the international working group on mild cognitive impairment. J.Intern. Med. 256(3), 240–246 (2004)
Acknowledgements
This work was partially funded by INFN within the MAGIC-5 research project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Aiello, M. et al. (2011). A Novel Template-Based Approach to the Segmentation of the Hippocampal Region. In: Tavares, J., Jorge, R. (eds) Computational Vision and Medical Image Processing. Computational Methods in Applied Sciences, vol 19. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0011-6_13
Download citation
DOI: https://doi.org/10.1007/978-94-007-0011-6_13
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-0010-9
Online ISBN: 978-94-007-0011-6
eBook Packages: EngineeringEngineering (R0)