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68 Chapter 3 Hippocampal segmentation using multiple atlases DSC 0.82 0.83 0.84 0.85 0.86 0.87 20 40 60 80 100 120 No. of selected atlases λ=0.1 λ=0.3 λ=0.5 λ=0.7 λ=0.9 λ=1 Figure 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. atlases selected according to the image similarity ranking. Combining the atlases selected by MMR reaches accuracy that required more atlases based on similarity selection. MMR is therefore a more efficient strategy when the number of atlases is restricted by the limitations of computation time, memory usage or the availability of atlases. The effect of using MMR is less significant on the AD atlas set. It may be due to more variability within the AD atlases introducing more noise into the penalty term of MMR. The LAR criterion achieved better performance when there were 20–40 atlases were selected and fused on the AD atlases. As a parametric method, MMR is dependent upon the parameter weighting the inter-atlas similarity. In terms of computation complexity, the LAR algorithm is more efficient since it does not require the step to pre-compute the inter-atlas similarity matrix as in MMR based method.
Chapter 3 Hippocampal segmentation using multiple atlases 69 DSC DSC 0.83 0.84 0.85 0.86 0.87 20 40 60 80 100 120 No. of selected atlases NMI Correlation LAR MMR−NMI λ=0.7 MMR−Correlation λ=0.7 (a) Performance of atlas selection strategies on NC atlases. 0.81 0.82 0.83 0.84 0.85 20 40 60 80 No. of selected atlases NMI Correlation LAR MMR−NMI λ=0.7 MMR−Correlation λ=0.7 (b) Performance of atlas selection strategies on AD atlases. Figure 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. 3.5 Summary We used a supervised approach to produce a set of population specific atlases from elderly subjects using multi-atlas based segmentation-propagation. Starting with 18 IBSR atlases, 16 images from elderly population well segmented were added to the atlas set. More images were added in the second iteration. The result
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UNIVERSITE DE BOURGOGNE U.F.R. SCIE
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Résumé L’objectif de cette thè
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Abstract The aim of this thesis is
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TABLE DES MATIÈRES Acknowledgement
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TABLE DES MATIÈRES xi 4.1.4.3 Spec
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LISTE DES FIGURES 1.1 Cortical atro
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LISTE DES FIGURES xvii 5.6 The accu
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2 Chapter 1 Introduction The resear
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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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