Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Hippocampus Segmentation and Classification in Alzheimer's Disease and Mild Cognitive Impairment Applied on MR Images

  • Madusanka, Nuwan (Department of Computer Engineering, Inje University) ;
  • Choi, Yu Yong (National Research Center for Dementia, Chosun University) ;
  • Choi, Kyu Yeong (National Research Center for Dementia, Chosun University) ;
  • Lee, Kun Ho (National Research Center for Dementia, Chosun University) ;
  • Choi, Heung-Kook (Department of Computer Engineering, Inje University)
  • Received : 2016.12.28
  • Accepted : 2017.01.31
  • Published : 2017.02.28
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Abstract

The brain magnetic resonance images (MRI) is an important imaging biomarker in Alzheimer's disease (AD) as the cerebral atrophy has been shown to strongly associate with cognitive symptoms. The decrease of volume estimates in different structures of the medial temporal lobe related to memory correlates with the decline of cognitive functions in neurodegenerative diseases. During the past decades several methods have been developed for quantifying the disease related atrophy of hippocampus from MRI. Special effort has been dedicated to separate AD and mild cognitive impairment (MCI) related modifications from normal aging for the purpose of early detection and prediction. We trained a multi-class support vector machine (SVM) with probabilistic outputs on a sample (n = 58) of 20 normal controls (NC), 19 individuals with MCI, and 19 individuals with AD. The model was then applied to the cross-validation of same data set which no labels were known and the predictions. This study presents data on the association between MRI quantitative parameters of hippocampus and its quantitative structural changes examination use on the classification of the diseases.

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References

  1. C.P. Hughes, L. Berg, W.L. Danziger, L.A. Coben, and R.L. Martin, "A New Clinical Scale for the Staging of Dementia," The British Journal of Psychiatry, Vol. 140, pp. 566-572, 1982. https://doi.org/10.1192/bjp.140.6.566
  2. M. Folstein, S.E. Folstein, and P.R. McHugh, "Mini-mental State: A Practical Method for Grading the Cognitive State of Patients for the Clinician," Journal of Psychiatric Research, Vol. 12, No. 3, pp. 189-198, 1975. https://doi.org/10.1016/0022-3956(75)90026-6
  3. J.M. Schott, S.L. Price, C. Frost, J.L. Whitwell, M.N. Rossor, and N.C. Fox, "Measuring Atrophy in Alzheimer Diseases: A Serial MRI Study over 6 and 12 Months," Neurology, Vol. 65, No. 1, pp. 119-124, 2005. https://doi.org/10.1212/01.wnl.0000167542.89697.0f
  4. J.C. Pruessner, D.L. Collins, M. Pruessner, and A.C. Evans, "Age and Gender Predict Volume Decline in the Anterior and Posterior Hippocampus," Journal of Neuroscience, Vol. 21, No. 1, pp. 194-200, 2001. https://doi.org/10.1523/JNEUROSCI.21-01-00194.2001
  5. T. Selma, N. Madusanka, T.H. Kim, Y.H. Kim, C.W. Mun, and H.K. Choi, "Contrast-enhanced Bias-corrected Distance-regularized Level Set Method Applied to Hippocampus Segmentation," Journal of Korea Multimedia Society, Vol. 19, No. 8, pp. 1236-1247, 2016. https://doi.org/10.9717/kmms.2016.19.8.1236
  6. C.R. Jack, M.M. Shiung, J.L. Gunter, P.C. O'Brien, S.D. Weigand, D.S. Knopman, et al, "Comparison of Different MRI Brain Atrophy Rate Measures with Clinical Disease Progression in AD," Neurology, Vol. 62, No. 4, pp. 591-600, 2004. https://doi.org/10.1212/01.WNL.0000110315.26026.EF
  7. J. Yang, L.H. Staib, and J.S. Duncan, "Neighbor- constrained Segmentation with Level Set Based 3D Deformable Models," IEEE Transactions on Medical Imaging, Vol. 23, No. 8, pp. 940-948, 2004. https://doi.org/10.1109/TMI.2004.830802
  8. Y.S. Izmantoko, H.S. Yoon, E. Adiya, C.W. Mun, Y. Huh, and H.K. Choi, "Implementation of 2D Active Shape Model-based Segmentation on Hippocampus," Journal of Korea Multimedia Society, Vol. 17, No. 1, pp. 1-7, 2014. https://doi.org/10.9717/kmms.2014.17.1.001
  9. M. Benjelloun, S. Mahmoudi, and F. Lecron, "A Framework of Vertebral Segmentation Using the Active Shape Model-based Approach," International Journal of Biomedical Imaging, Vol. 2011, pp. 1-14, 2011.
  10. P.A. Yushkevich, J.A. Detre, D.M. Hamilton, M.A.F. Seara, K.Z. Tang, A. Hoang, et al, "Hippocampus-Specific fMRI Group Activation Analysis Using the Continuous Medial Representation," NeuroImage, Vol. 35, No. 4, pp. 1516-1530, 2007. https://doi.org/10.1016/j.neuroimage.2007.01.029
  11. S. Bouix, J.C. Pruessner, D.L. Collins, and K.Siddiqi, "Hippocampal Shape Analysis Using Medial Surfaces," NeuroImage, Vol. 25, No. 4, pp. 1077-1089, 2005. https://doi.org/10.1016/j.neuroimage.2004.12.051
  12. J.C. Mazziotta, A. Toga, A. Evans, P. Fox, J. Lancaster, K. Zilles, et al, "A Probabilistic Atlas and Reference System for the Human Brain: International Consortium for Brain Mapping (ICBM)," Philosophical Transactions of the Royal Society of London, Vol. 356, No. 1412, pp. 1293-1322, 2001. https://doi.org/10.1098/rstb.2001.0915
  13. K.H. Fritzsche, A. Von Wangenheim, D.D. Abdala, and H.P. Meinzer, "A Computational Method for the Estimation of Atrophic Changes in Alzheimer's Disease and Mild Cognitive Impairment," Computerized Medical Imaging and Graphics, Vol. 32, No. 4, pp. 294-303, 2008. https://doi.org/10.1016/j.compmedimag.2007.12.006
  14. J. Wang, A. Ekin, and G. deHaan, "Shape Analysis of Brain Ventricles for Improved Classification of Alzheimer's Patients," Proceeding of International Conference Image Processing, pp. 2252-2255, 2008.
  15. C. Cortes and V. Vapnik, "Support-Vector Networks," Machine Learning, Vol. 20, pp. 273-297, 1995.
  16. J. Czajkowska, M. Rudzki, and Z. Czajkowski, "A New Fuzzy Support Vectors Machine for Biomedical Classification," Proceeding of 30th Annual International IEEE EMBS Conference, pp. 4476-4479, 2008.
  17. F. Bovolo, L. Bruzzone, and L. Carlin, "A Novel Technique for Subpixel Image Classification Based on Support Vector Machine," IEEE Transaction on Image Processing, Vol. 19, No. 11, pp. 2983-2999, 2010. https://doi.org/10.1109/TIP.2010.2051632
  18. V. Vapnik, "An Overview of Statistical Learning Theory," IEEE Transactions on Neural Networks, Vol. 10, No. 5, pp. 988-999, 1999. https://doi.org/10.1109/72.788640

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