Abstract
Many medical applications commonly make use of brain magnetic resonance images (MRI) as an information source since they provide a non-invasive view of the head morphology and functionality. Such information is given by the properties of head structures, which are extracted using segmentation techniques. Among them, multi-atlas-based methodologies are the most popular, allowing to consider prior spatial information about the distribution of brain structures. These approaches rely on a non-linear mapping of the information of the most relevant atlases to a query image. Nevertheless, methodology effectiveness is highly dependent on the mapping function and the atlas relevance criterion, being both of them based on the selection of an MRI similarity metric. Here, a new spatially weighting measure is proposed to enhance the multi-atlas-based segmentation results. The proposal is tested in an MRI segmentation database for state-of-the-art image metrics as means squares, histogram correlation coefficient, normalized mutual information, and neighborhood cross-correlation and compared against other spatial combination approaches. Achieved results show that our proposal outperforms baseline methods, providing a more suitable atlas selection.
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References
Bron, E.E., Cardenas-Pena, D., et al.: Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural mri: The CADDementia challenge. NeuroImage (2015)
Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23(suppl. 1), S151–S60 (2004)
Rohlfing, T., Brandt, R., Menzel, R., Russakoff, D., Jr., M.C.: Quo vadis, atlas-based segmentation? In: Suri, J., Wilson, D., Laxminarayan, S. (eds.) Handbook of Biomedical Image Analysis. Topics in Biomedical Engineering International Book Series, pp. 435–486. Springer US (2005)
Warfield, S.K., Zou, K.H., Wells, W.M.: IEEE Transactions on Medical Imaging
Artaechevarria, X., Munoz-Barrutia, A., Ortiz-de Solorzano, C. IEEE transactions on medical imaging
Aljabar, P., Heckemann, R.A., Hammers, A., Hajnal, J.V., Rueckert, D.: NeuroImage
Wu, M., Rosano, C., Lopez-Garcia, P., Carter, C.S., Aizenstein, H.J.: NeuroImage
van Rikxoort, E.M., Isgum, I., Arzhaeva, Y., Staring, M., Klein, S., Viergever, M.A., Pluim, J.P.W., van Ginneken, B.: Medical image analysis
Avants, B., Tustison, N., Song, G., Cook, P., Klein, A., Gee, J.: A reproducible evaluation of ants similarity metric performance in brain image registration. NeuroImage 54, 2033–2044 (2011) cited By 139
Studholme, C., Drapaca, C., Iordanova, B., Cardenas, V.: Deformation-Based Mapping of Volume Change From Serial Brain MRI in the Presence of Local Tissue Contrast Change, 626–639 (2006)
Cortes, C., Mohri, M., Rostamizadeh, A.: Algorithms for learning kernels based on centered alignment. The Journal of Machine Learning Research 13(1), 795–828 (2012)
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Cárdenas-Peña, D., Orbes-Arteaga, M., Castellanos-Dominguez, G. (2015). Supervised Brain Tissue Segmentation Using a Spatially Enhanced Similarity Metric. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Artificial Computation in Biology and Medicine. IWINAC 2015. Lecture Notes in Computer Science(), vol 9107. Springer, Cham. https://doi.org/10.1007/978-3-319-18914-7_42
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DOI: https://doi.org/10.1007/978-3-319-18914-7_42
Publisher Name: Springer, Cham
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