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xvi LISTE DES FIGURES 3.7 The average Dice similarity coefficient (DSC) of left and right hippocampi using locally weighted voting (LWV) on the normal control (NC) atlas set. The atlases selected by maximal marginal relevance (MMR) re-ranking. The parameter λ varies from 0.1 to 0.9. The case of λ = 1 is equivalent to the selection by NMI ranking. . . . . 68 3.8 The average Dice similarity coefficient (DSC) of left and right hippocampi using locally weighted voting (LWV) on the normal control (NC) atlas set. The atlases are selected according to image similarity ranking, maximal marginal relevance (MMR) re-ranking and least angle regression sequence. . . . . . . . . . . . . . . . . . . . . 69 4.1 Parameterizations and reparameterizations of the shape surfaces and correspondences. . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.2 Parameterization of quadrilateral. . . . . . . . . . . . . . . . . . . . 78 4.3 Multi-resolution subsampling by icosahedron subdivision. . . . . . . 80 4.4 Cutting the octahedron and unfold to a plane. . . . . . . . . . . . . 81 4.5 The construction of a shape image, from shape to sphere to embedded octahedron to final shape image. . . . . . . . . . . . . . . . . . 82 4.6 Reparameterization on the spherical parameterization and on the shape image. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.7 Six possible choices of reorientation and unfolding of the octahedron. 88 4.8 The compactness of the Statistical Shape Model (SSM). . . . . . . 98 4.9 The behavior of the Specificity(M) in the groupwise optimization of the Statistical Shape Model (SSM), using the first M = 15 principal components, the number of trials in the simulation N = 1000. . . . 99 4.10 The descendance of Generalisability(M) in the groupwise optimization of the Statistical Shape Model (SSM), using the first M = 15 principal components. . . . . . . . . . . . . . . . . . . . . . . . . . 100 4.11 The shape variation in the first 3 modes of the left hippocampal shape model (±2 standard deviations). . . . . . . . . . . . . . . . . 101 4.12 The precision of the model reconstruction with the estimated shape parameter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 4.13 An example of reconstruction from estimated shape parameters. . . 105 4.14 An example of regularization on symmetric shape parameter estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 5.1 The pipeline of local shape descriptor extraction. . . . . . . . . . . 112 5.2 The significance map of local difference on hippocampal surface by Hotelling’s T 2 test. . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 5.3 Thresholded significance map, by thresholding p-values in the significance map Figure 5.2. . . . . . . . . . . . . . . . . . . . . . . . . 122 5.4 The percentage of landmarks selected, with varying thresholds in the landmark selection on the significance map of Hotelling’s T 2 test.123 5.5 The training accuracy of the disease classification using bagged support vector machines (SVMs) with varying thresholds in the landmark selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
LISTE DES FIGURES xvii 5.6 The accuracy of the disease classification using bagged support vector machines (SVMs) with varying thresholds in the landmark selection on a separated testing set. . . . . . . . . . . . . . . . . . . . 125 5.7 The out-of-bag (OOB) estimates of sensitivity of the disease classification using bagged support vector machines (SVMs) with varying thresholds in the landmark selection. . . . . . . . . . . . . . . . . . 126 5.8 The sensitivity of the disease classification using bagged support vector machines (SVMs) with varying thresholds in the landmark selection on a separated testing set. . . . . . . . . . . . . . . . . . . 127 5.9 The out-of-bag (OOB) estimates of specificity of the disease classification using bagged support vector machines (SVMs) with varying thresholds in the landmark selection. . . . . . . . . . . . . . . . . . 128 5.10 The specificity of the disease classification using bagged support vector machines (SVMs) with varying thresholds in the landmark selection on a separated testing set. . . . . . . . . . . . . . . . . . . 129 5.11 Bar plots of Spearman’s ρ of the correlation between the memory indices and the principal components from each model. . . . . . . . 130 5.12 Variation modes captured by Principal Component Analysis (PCA) on hippocampal subregions best correlated to memory indices (logical memory, AVLT, ADAS memory), color-coded map showing the magnitude of variation described by the mode. . . . . . . . . . . . . 131 5.13 Three-dimensional representation of hippocampal subfields by La Joie et al., 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 5.14 The variation along the direction of mean difference between the normal control (NC) group and the Alzheimer’s disease (AD) group, rigidly aligned model. . . . . . . . . . . . . . . . . . . . . . . . . . . 134 5.15 The variation along the direction of mean difference between the normal control (NC) group and the Alzheimer’s disease (AD) group, similarity aligned model. . . . . . . . . . . . . . . . . . . . . . . . . 135
Page 1: UNIVERSITE DE BOURGOGNE U.F.R. SCIE
Page 5 and 6: Résumé L’objectif de cette thè
Page 7 and 8: Abstract The aim of this thesis is
Page 9 and 10: TABLE DES MATIÈRES Acknowledgement
Page 11: TABLE DES MATIÈRES xi 4.1.4.3 Spec
Page 15: LISTE DES FIGURES 1.1 Cortical atro
Page 20 and 21: 2 Chapter 1 Introduction The resear
Page 22 and 23: 4 Chapter 1 Introduction .β.α .γ
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Page 26 and 27: 8 Chapter 1 Introduction Figure 1.4
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Chapter 6 Conclusions 139 Given the
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Chapter 6 Conclusions 141 6.2.3 Sha
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144 LIST OF PUBLICATIONS using dive
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146 BIBLIOGRAPHY Alzheimer, A. (191
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148 BIBLIOGRAPHY Baillard, C., Hell
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150 BIBLIOGRAPHY of post mortem mag
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152 BIBLIOGRAPHY B. (2008). Three-d
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154 BIBLIOGRAPHY Cootes, T. F., Tay
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156 BIBLIOGRAPHY head in MR images
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158 BIBLIOGRAPHY Evans, A. C., Coll
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BIBLIOGRAPHY 161 Ghanei, A., Soltan
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BIBLIOGRAPHY 163 Heckemann, R. A.,
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BIBLIOGRAPHY 165 R. C. (2003). MRI
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BIBLIOGRAPHY 167 Konrad, C., Ukas,
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BIBLIOGRAPHY 169 Mahony, R. and Man
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BIBLIOGRAPHY 171 disease, mild cogn
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BIBLIOGRAPHY 173 Petersen, R. C., S
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BIBLIOGRAPHY 175 Rorden, C. and Bre
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BIBLIOGRAPHY 177 Shen, K., Bourgeat
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BIBLIOGRAPHY 179 Barillot, C., Hayn
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BIBLIOGRAPHY 181 Tzourio-Mazoyer, N
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BIBLIOGRAPHY 183 Vos, F. M., de Bru
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BIBLIOGRAPHY 185 Woolrich, M. W., J
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