Probability-based Scoring for Normality Map in Brain MRI Images from Normal Control Population

Thach-Thao Duong

Abstract

The increasing availability of MRI brain data opens up a research direction for abnormality detection which is necessary to on-time detection of impairment and performing early diagnosis. The paper proposes scores based on z-score transformation and kernel density estimation (KDE) which are respectively Gaussian-based assumption and nonparametric modeling to detect the abnormality in MRI brain images. The methodologies are applied on gray-matter-based score of Voxel-base Morphometry (VBM) and sparse-based score of Sparse-based Morphometry (SBM). The experiments on well-designed normal control (CN) and Alzheimer disease (AD) subsets extracted from MRI data set of Alzheimer’s Disease Neuroimaging Initiative (ADNI) are conducted with threshold-based classification. The analysis of abnormality percentage of AD and CN population is carried out to validate the robustness of the proposed scores. The further cross validation on Linear discriminant analysis (LDA) and Support vector machine (SVM) classification between AD and CN show significant accuracy rate, revealing the potential of statistical modeling to measure abnormality from a population of normal subjects.

References

  1. Anscombe, F. J. and Guttman, I. (1960). Rejection of outliers. 2(2):123-147.
  2. Ashburner, J. and Friston, K. J. (2000). Voxel-based morphometrythe methods. NeuroImage, 11(6):805-821.
  3. Braak, H. and Braak, E. (1991). Neuropathological stageing of alzheimer-related changes. Acta Neuropathologica, 82(4):239-259.
  4. Cortes, C. and Vapnik, V. (7895). Support-vector networks. Mach. Learn., 20(3):273-297.
  5. Deshpande, H., Maurel, P., and Barillot, C. (2015). Classification of Multiple Sclerosis Lesions using Adaptive Dictionary Learning. Computerized Medical Imaging and Graphics, pages 1-15.
  6. Devijver, P. A. and Kittler, J. (1982). Pattern recognition: A statistical approach. Prentice Hall.
  7. Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(7):179- 188.
  8. Gooda, C. D., Johnsrudeb, I., Ashburnera, J., Hensona, R. N., Fristona, K. J., and Frackowiaka, R. S. (2001). A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging. NeuroImage, 14(3):685-700.
  9. Huttona, C., Draganskia, B., Ashburnera, J., and Weiskopfa, N. (2009). A comparison between voxelbased cortical thickness and voxel-based morphometry in normal aging. NeuroImage, 48(2):371-380.
  10. Irimia, A., Wang, B., Aylward, S. R., Prastawa, M. W., Pace, D. F., Gerig, G., Hovda, D. A., Kikinis, R., Vespa, P. M., and Horn, J. D. V. (2012). Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction. NeuroImage: Clinical, 1(1):1 - 17.
  11. Liu, X., Niethammer, M., Kwitt, R., McCormick, M., and Aylward, S. R. (2014). Low-rank to the rescue - atlasbased analyses in the presence of pathologies. In MICCAI (3)7814, pages 97-104.
  12. Mairal, J., Bach, F., Ponce, J., and Sapiro, G. (2009). Online dictionary learning for sparse coding. In Proceedings of the 26th ICML, pages 689-696.
  13. Parzen, E. (1962). On estimation of a probability density function and mode. The Annals of Mathematical Statistics, 33(3):pp. 1065-1076.
  14. Radua, J. and Mataix-Cols, D. (2009). Voxel-wise meta-analysis of grey matter changes in obsessivecompulsive disorder. The British J. of Psychiatry, 195(5):393-402.
  15. Rosenblatt, M. (1956). Remarks on some nonparametric estimates of a density function. The Annals of Mathematical Statistics, 27(3):832-837.
  16. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., and Joliot, M. (2002). Automated anatomical labeling of activations in {SPM} using a macroscopic anatomical parcellation of the {MNI} {MRI} single-subject brain. NeuroImage, 15(1):273 - 289.
  17. Weiss, N., Rueckert, D., and Rao, A. (2013). Multiple sclerosis lesion segmentation using dictionary learning and sparse coding. In Medical Image Computing and Computer-Assisted Intervention - MICCAI, pages 735-742.
  18. Wilke, M., Rose, D. F., Holland, S. K., and Leach, J. L. (2014). Multidimensional morphometric 3d mri analyses for detecting brain abnormalities in children: Impact of control population. Human Brain Mapping, 35(7):3199-3215.
  19. Wyman, B. T., Harvey, D. J., Crawford, K., Bernstein, M. A., Carmichael, O., Cole, P. E., Crane, P. K., DeCarli, C., Fox, N. C., Gunter, J. L., Hill, D., Killiany, R. J., Pachai, C., Schwarz, A. J., Schuff, N., Senjem, M. L., Suhy, J., Thompson, P. M., Weiner, M., and Jr., C. R. J. (2013). Standardization of analysis sets for reporting results from ADNI MRI data. Alzheimer's & Dementia, 9(3):332 - 337.
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Paper Citation


in Harvard Style

Duong T. (2016). Probability-based Scoring for Normality Map in Brain MRI Images from Normal Control Population . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 254-261. DOI: 10.5220/0005724702540261


in Bibtex Style

@conference{visapp16,
author={Thach-Thao Duong},
title={Probability-based Scoring for Normality Map in Brain MRI Images from Normal Control Population},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={254-261},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005724702540261},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Probability-based Scoring for Normality Map in Brain MRI Images from Normal Control Population
SN - 978-989-758-175-5
AU - Duong T.
PY - 2016
SP - 254
EP - 261
DO - 10.5220/0005724702540261