]. H. Völzke, Population Imaging as Valuable Tool for Personalized Medicine Challenges of Cardiac Image Analysis in Large-Scale Population-Based Studies Precision Imaging: more descriptive, predictive and integrative imaging, Clinical Pharmacology & Therapeutics Curr Cardiol Rep Medical Image Analysis, vol.924, issue.33, pp.422-424, 2012.

C. Petitjean, Right ventricle segmentation from cardiac MRI: A collation study, Medical Image Analysis, vol.19, issue.1, pp.187-202, 2015.
DOI : 10.1016/j.media.2014.10.004
URL : https://hal.archives-ouvertes.fr/hal-01081337

J. J. Cerrolaza, Automatic multi-resolution shape modeling of multi-organ structures Congenital Aortic Disease: 4D Magnetic Resonance Segmentation and Quantitative Analysis Voxelwise atlas rating for computer assisted diagnosis: Application to congenital heart diseases of the great arteries Automatic Multi-model-Based Segmentation of the Left Atrium in Cardiac MRI Scans Cluster analysis and display of genome-wide expression patterns, Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2012, pp.11-21, 1998.

F. Murtagh and P. Contreras, Algorithms for hierarchical clustering: an overview, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol.30, issue.1, pp.86-97, 2012.
DOI : 10.1002/widm.53

A. K. Jain, Data clustering: 50 years beyond K-means, Pattern Recognition Letters, vol.31, issue.8, pp.651-666, 2010.
DOI : 10.1016/j.patrec.2009.09.011
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.151.4286

T. Hastie, The Elements of Statistical Learning, 2009.

M. Halkidi, On Clustering Validation Techniques, Journal of Intelligent Information Systems, vol.17, issue.2/3, pp.107-145, 2001.
DOI : 10.1023/A:1012801612483

M. Brun, Model-based evaluation of clustering validation measures, Pattern Recognition, vol.40, issue.3, pp.807-824, 2007.
DOI : 10.1016/j.patcog.2006.06.026
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.80.2871

L. Dalton, Clustering Algorithms: On Learning, Validation, Performance, and Applications to Genomics, Current Genomics, vol.10, issue.6, pp.430-445, 2009.
DOI : 10.2174/138920209789177601
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2766793

A. Srivastava, Statistical shape analysis: clustering, learning, and testing, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.4, pp.590-602, 2005.
DOI : 10.1109/TPAMI.2005.86
URL : http://calais.stat.fsu.edu/anuj/PDF-files/Papers/SrivastavaShapeAnalysis.pdf

A. Dong, CHIMERA: Clustering of Heterogeneous Disease Effects via Distribution Matching of Imaging Patterns, IEEE Transactions on Medical Imaging, vol.35, issue.2, pp.612-621, 2016.
DOI : 10.1109/TMI.2015.2487423

D. Broggio, Comparison of organs??? shapes with geometric and Zernike 3D moments, Computer Methods and Programs in Biomedicine, vol.111, issue.3, pp.740-754, 2013.
DOI : 10.1016/j.cmpb.2013.06.005

P. Ou, Late systemic hypertension and aortic arch geometry after successful repair of coarctation of the aorta, European Heart Journal, vol.25, issue.20, pp.1853-1859, 2004.
DOI : 10.1016/j.ehj.2004.07.021

Y. Lecompte, Anatomic correction of transposition of the great arteries, J. Thorac. Cardiovasc. Surg, vol.82, issue.4, pp.629-631, 1981.

J. L. Bruse, A Non-parametric Statistical Shape Model for Assessment of the Surgically Repaired Aortic Arch in Coarctation of the Aorta: How Normal is Abnormal?, Statistical Atlases and Computational Models of the Heart 2015, pp.21-29
DOI : 10.1007/978-3-319-28712-6_3
URL : https://hal.archives-ouvertes.fr/hal-01205515

H. N. Ntsinjana, 3D morphometric analysis of the arterial switch operation using in vivo MRI data, Clinical Anatomy, vol.2, issue.8, pp.1212-1222, 2014.
DOI : 10.1002/ca.22458

M. Vaillant and J. Glaunès, Surface Matching via Currents, " in Information Processing in Medical Imaging, pp.381-392, 2005.
DOI : 10.1007/11505730_32
URL : http://cis.jhu.edu/software/lddmm-surface/ipmi05.pdf

S. Durrleman, Statistical models of sets of curves and surfaces based on currents, Medical Image Analysis, vol.13, issue.5, pp.793-808, 2009.
DOI : 10.1016/j.media.2009.07.007
URL : https://hal.archives-ouvertes.fr/hal-00816051

M. A. Zuluaga, Multi-atlas Propagation Whole Heart Segmentation from MRI and CTA Using a Local Normalised Correlation Coefficient Criterion, Functional Imaging and Modeling of the Heart, pp.174-181, 2013.
DOI : 10.1007/978-3-642-38899-6_21

P. A. Yushkevich, User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability, NeuroImage, vol.31, issue.3, pp.1116-1128, 2006.
DOI : 10.1016/j.neuroimage.2006.01.015

W. Schroeder, The Visualization Toolkit, 2006.
DOI : 10.1016/B978-012387582-2/50032-0

J. Ahrens, ParaView: An End-User Tool for Large-Data Visualization The Visualization Handbook, p.717, 2005.

L. Antiga, An image-based modeling framework for patient-specific computational hemodynamics, Medical & Biological Engineering & Computing, vol.29, issue.3, pp.1097-1112, 2008.
DOI : 10.1007/s11517-008-0420-1
URL : http://dx.doi.org/10.1007/s11517-008-0420-1

P. J. Besl and N. D. Mckay, A method for registration of 3-D shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.2, pp.239-256, 1992.
DOI : 10.1109/34.121791

J. L. Bruse, A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: assessing arch morphology of repaired coarctation of the aorta, BMC Medical Imaging, vol.167, issue.1, p.40, 2016.
DOI : 10.1186/s12880-016-0142-z
URL : https://hal.archives-ouvertes.fr/hal-01335147

S. Durrleman, Morphometry of anatomical shape complexes with dense deformations and sparse parameters, NeuroImage, vol.101, pp.35-49, 2014.
DOI : 10.1016/j.neuroimage.2014.06.043
URL : https://hal.archives-ouvertes.fr/hal-01015771

M. F. Beg, Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms, International Journal of Computer Vision, vol.61, issue.2, pp.139-157, 2005.
DOI : 10.1023/B:VISI.0000043755.93987.aa

M. Piccinelli, A Framework for Geometric Analysis of Vascular Structures: Application to Cerebral Aneurysms, IEEE Transactions on Medical Imaging, vol.28, issue.8, pp.1141-1155, 2009.
DOI : 10.1109/TMI.2009.2021652

T. Mansi, A Statistical Model for Quantification and Prediction of Cardiac Remodelling: Application to Tetralogy of Fallot, IEEE Transactions on Medical Imaging, vol.30, issue.9, pp.1605-1616, 2011.
DOI : 10.1109/TMI.2011.2135375
URL : https://hal.archives-ouvertes.fr/inria-00616185

I. T. Joliffe, Principal Component Analysis, 2002.
DOI : 10.1007/978-1-4757-1904-8

F. Murtagh, A Survey of Recent Advances in Hierarchical Clustering Algorithms, The Computer Journal, vol.26, issue.4, pp.354-359, 1983.
DOI : 10.1093/comjnl/26.4.354

M. R2011, T. Mathworks, and . Inc, MATLAB Help Documentation, 2011.

M. Sokolova and G. Lapalme, A systematic analysis of performance measures for classification tasks, Information Processing & Management, vol.45, issue.4, pp.427-437, 2009.
DOI : 10.1016/j.ipm.2009.03.002

D. M. Powers, Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation, Journal of Machine Learning Technologies, 2011.

P. Baldi, Assessing the accuracy of prediction algorithms for classification: an overview, Bioinformatics, vol.16, issue.5, pp.412-424, 2000.
DOI : 10.1093/bioinformatics/16.5.412

K. Y. Yeung and W. L. Ruzzo, Principal component analysis for clustering gene expression data, Bioinformatics, vol.17, issue.9, pp.763-774, 2001.
DOI : 10.1093/bioinformatics/17.9.763
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.103.2733

G. N. Lance and W. T. Williams, A General Theory of Classificatory Sorting Strategies: 1. Hierarchical Systems, The Computer Journal, vol.9, issue.4, pp.373-380, 1967.
DOI : 10.1093/comjnl/9.4.373

R. Dubes and A. K. Jain, Clustering techniques: The user's dilemma, Pattern Recognition, vol.8, issue.4, pp.247-260, 1976.
DOI : 10.1016/0031-3203(76)90045-5

E. R. Dougherty, Inference from Clustering with Application to Gene-Expression Microarrays, Journal of Computational Biology, vol.9, issue.1, pp.105-126, 2002.
DOI : 10.1089/10665270252833217
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.2.3235

D. M. Boyer, A New Fully Automated Approach for Aligning and Comparing Shapes, The Anatomical Record, vol.17, issue.5, pp.249-276, 2015.
DOI : 10.1002/ar.23084

M. Schecklmann, Cluster analysis for identifying sub-types of tinnitus: A positron emission tomography and voxel-based morphometry study, Brain Research, vol.1485, pp.3-9, 2012.
DOI : 10.1016/j.brainres.2012.05.013

N. S. Singh49 and ]. Tsymbal, Performance Evaluation of K-Means and Heirarichal Clustering in Terms of Accuracy and Running Time Towards cloud-based image-integrated similarity search in big data, 2014 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp.593-596, 2014.

X. Zhuang, Challenges and Methodologies of Fully Automatic Whole Heart Segmentation: A Review, Journal of Healthcare Engineering, vol.8669, issue.3, pp.371-407, 2013.
DOI : 10.1260/2040-2295.4.3.371